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Penalized empirical likelihood for high-dimensional generalized linear models Stat. Interface (IF 0.412) Pub Date : 2021-01-01 Xia Chen; Liyue Mao
We develop penalized empirical likelihood for parameter estimation and variable selection in high-dimensional generalized linear models. By using adaptive lasso penalty function, we show that the proposed estimator has the oracle property. Also, we consider the problem of testing hypothesis, and show that the nonparametric profiled empirical likelihood ratio statistic has asymptotic chi-square distribution
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Spatial regression models for bounded response variables with evaluation of the degree of dependence Stat. Interface (IF 0.412) Pub Date : 2021-01-01 Sandra E. Flores; Marcos O. Prates; Jorge L. Bazán; Heleno B. Bolfarine
Bounded response variables such as percentages, proportions, or rates are common in applications involving social and educational datasets, including rates of poverty or rates of achievement by municipalities, counties or provinces. New regression models have been proposed in recent years by considering distributions such as the Beta, Simplex and Kumaraswamy models for this type of data. However, to
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Relating parameters in conditional, marginalized, and marginal logistic models when the mediator is binary Stat. Interface (IF 0.412) Pub Date : 2021-01-01 Kai Wang
Stanghellini and Doretti (2019) studied the exact formulae relating parameters in conditional and marginalized logistic models when the mediator is binary. Those formulae generally do not hold for the reduced model as the reduced model is generally not the same as the marginalized model. For a conditional model that allows for treatmentmediator interaction, I present 1) alternative exact formulae relating
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Information diffusion with network structures Stat. Interface (IF 0.412) Pub Date : 2021-01-01 Zhu Xuening; Rui Pan; Yuxuan Zhang; Yu Chen; Wenquan Mi; Hansheng Wang
Information diffusion refers to the process about passing certain information from one subject to another. It is a typical and critical phenomenon observed in large scale social networks. To statistically model such a phenomenon, a network diffusion model is proposed and studied. The diffusion process is then investigated under the modeling framework from both the short term and long term perspectives
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Lead time distribution for individuals with a screening history Stat. Interface (IF 0.412) Pub Date : 2021-01-01 Ruiqi Liu; Dongfeng Wu; Shesh N. Rai
We derived the distribution of lead time for periodic screening in the future when an individual has a screening history with negative results. It is a mixture of a point mass at zero and a positive sub-PDF. The motivation comes from the reality that for people in older age, they may already have some screening exams for targeted cancer before and still look healthy and are asymptomatic at their current
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Bayesian zero-inflated growth mixture models with application to health risk behavior data Stat. Interface (IF 0.412) Pub Date : 2021-01-01 Si Yang; Gavino Puggioni
This paper focuses on developing latent class models for longitudinal data with zero-inflated count response variables. The goals are to model discrete longitudinal patterns of rare events counts (for instance, health-risky behavior), and to identify individual-specific covariates associated with latent class probabilities. Two discrete latent structures are present in this type of model: a latent
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Estimation and diagnostics for partially linear censored regression models based on heavy-tailed distributions Stat. Interface (IF 0.412) Pub Date : 2021-01-01 Marcela Nuñez Lemus; Victor H. Lachos; Christian E. Galarza; Larissa A. Matos
In many studies, limited or censored data are collected. This occurs, in several practical situations, for reasons such as limitations of measuring instruments or due to experimental design. So, the responses can be either left, interval or right censored. On the other hand, partially linear models are considered as a flexible generalizations of linear regression models by including a nonparametric
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Estimating equation estimators of quantile differences for one sample with length-biased and right-censored data Stat. Interface (IF 0.412) Pub Date : 2021-01-01 Dehui Wang; Li Xun; Guangchao Zhang; Yong Zhou
This paper estimates quantile differences for one sample with length-biased and right-censored (LBRC) data. To ensure the asymptotic unbiasedness of the estimator, the estimating equation method is adopted. To improve the efficiency of the estimator, in the sense of having a lower mean squared error, the kernel-smoothed approach is employed. To make full use of the features of LBRC data, the augmented
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A non-marginal variable screening method for the varying coefficient Cox model Stat. Interface (IF 0.412) Pub Date : 2021-01-01 Lianqiang Qu; Liuquan Sun
The varying coefficient model has become a very popular statistical tool for describing the dynamic effects of covariates on the response. In this article, we develop a new variable screening method for the varying coefficient Cox model based on the kernel smoothing and group learning methods. The sure screening property is established for ultra-high-dimensional settings. In addition, an iterative
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Grouped variable selection with prior information via the prior group bridge method Stat. Interface (IF 0.412) Pub Date : 2021-01-01 Kai Li; Meng Mei; Yuan Jiang
In a multiple regression with grouped predictors, it is usually desired to select important groups as well as to select important variables within a group simultaneously. To achieve this so-called “bi-level selection,” group bridge has been developed as a combination of group-level bridge and variable-level lasso penalties. However, in many scientific areas, prior knowledge is available about the importance
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The timing and effectiveness of implementing mild interventions of COVID-19 in large industrial regions via a synthetic control method Stat. Interface (IF 0.412) Pub Date : 2021-01-01 Ting Tian; Wenxiang Luo; Jianbin Tan; Yukang Jiang; Minqiong Chen; Wenliang Pan; Songpan Yang; Jiashu Zhao; Xueqin Wang; Heping Zhang
The outbreak of novel coronavirus disease (COVID-19) has spread around the world since it was detected in December 2019. The Chinese government executed a series of interventions to curb the pandemic. The “battle” against COVID-19 in Shenzhen, China is valuable because populated industrial cities are the epic centres of COVID-19 in many regions. We made use of synthetic control methods to create a
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Discussion on “The timing and effectiveness of implementing mild interventions of COVID-19 in large industrial regions via a synthetic control method” by Tian et al. Stat. Interface (IF 0.412) Pub Date : 2021-01-01 Kun Chen; Fei Wang
The ongoing pandemic of the novel coronavirus disease 2019 (COVID-19) has impacted tens of millions of people and caused a huge economic loss. Most of the impacted countries have implemented different non-pharmaceutical interventions (NPIs) to control and prevent the spreading of SARS-Cov-2, which is the virus causing COVID-19. With the coming flu season in the northern hemisphere, many countries are
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Discussion on “The timing and effectiveness of implementing mild interventions of COVID-19 in large industrial regions via a synthetic control method” by Tian et al. Stat. Interface (IF 0.412) Pub Date : 2021-01-01 Yifan Zhu
This article provides an overview and discussion of the recent published paper from Tian et al. on modeling the differences of COVID-19 outbreak between Shenzhen and a synthetic population constructed from 68 US counties.
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Discussion on “The timing and effectiveness of implementing mild interventions of COVID-19 in large industrial regions via a synthetic control method” by Tian et al. Stat. Interface (IF 0.412) Pub Date : 2021-01-01 Soumik Purkayastha; Peter Song
It is a pleasure to have the opportunity to comment on this contribution by [6]. The authors use a matching technique called the synthetic control method (SCM) [1] to compare the spread of the COVID-19 pandemic in Shenzhen, China with a synthetic reference population in the USA that matches certain characteristics of Shenzhen, as chosen by the authors. The primary goal of this analysis is to examine
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Discussion on “The timing and effectiveness of implementing mild interventions of COVID-19 in large industrial regions via a synthetic control method” by Tian et al. Stat. Interface (IF 0.412) Pub Date : 2021-01-01 Debashree Ray; Rupam Bhattacharyya; Bhramar Mukherjee
Tian et al. ought to be commended for their approach of using synthetic control methodology (SCM) to evaluate effectiveness of mild intervention strategies (e.g. wearing masks, isolation of overseas travelers, etc.) in controlling the spread of COVID-19 in industrial regions. The authors use Shenzhen in the Guangdong province of China as an example and compare it with several control counties in the
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The need to incorporate communities in compartmental models Stat. Interface (IF 0.412) Pub Date : 2021-01-01 Michael J. Kane; Owais Gilani
Tian et al. provide a framework for assessing populationlevel interventions of disease outbreaks through the construction of counterfactuals in a large-scale, natural experiment assessing the efficacy of mild, but early interventions compared to delayed interventions. The technique is applied to the recent SARS-CoV-2 outbreak with the population of Shenzhen, China acting as the mild-but-early treatment
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A novel intervention recurrent autoencoder for real time forecasting and non-pharmaceutical intervention selection to curb the spread of Covid-19 in the world Stat. Interface (IF 0.412) Pub Date : 2021-01-01 Qiyang Ge; Zixin Hu; Shudi Li; Wei Lin; Li Jin; Momiao Xiong
As the Covid-19 pandemic soars around the world, there is urgent need to forecast the number of cases worldwide at its peak, the length of the pandemic before receding and implement public health interventions to significantly stop the spread of Covid-19. Widely used statistical and computer methods for modeling and forecasting the trajectory of Covid-19 are epidemiological models. Although these epidemiological
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A robust nonlinear mixed-effects model for COVID-19 death data Stat. Interface (IF 0.412) Pub Date : 2021-01-01 Fernanda L. Schumacher; Clécio S. Ferreira; Marcos O. Prates; Alberto Lachos; Victor H. Lachos
The analysis of complex longitudinal data such as COVID-19 deaths is challenging due to several inherent features: (i) similarly-shaped profiles with different decay patterns; (ii) unexplained variation among repeated measurements within each country, possibly interpreted as clustered data since they are obtained from the same country at roughly the same time; and (iii) skewness, outliers or skewed
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Forecasting confirmed cases of the COVID-19 pandemic with a migration-based epidemiological model Stat. Interface (IF 0.412) Pub Date : 2021-01-01 Xinyu Wang; Lu Yang; Hong Zhang; Zhouwang Yang; Catherine Liu
The unprecedented coronavirus disease 2019 (COVID-19) pandemic is still a worldwide threat to human life since its invasion into the daily lives of the public in the first several months of 2020. Predicting the size of confirmed cases is important for countries and communities to make proper prevention and control policies so as to effectively curb the spread of COVID-19. Different from the 2003 SARS
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Heterogeneity learning for SIRS model: an application to the COVID-19 Stat. Interface (IF 0.412) Pub Date : 2021-01-01 Guanyu Hu; Junxian Geng
We propose a Bayesian Heterogeneity Learning approach for Susceptible-Infected-Removal-Susceptible (SIRS) model that allows underlying clustering patterns for transmission rate, recovery rate, and loss of immunity rate for the latest corona virus (COVID-19) among different regions. Our proposed method provides simultaneously inference on parameter estimation and clustering information which contains
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On evidence cycles in network meta-analysis. Stat. Interface (IF 0.412) Pub Date : 2020-07-31 Lifeng Lin,Haitao Chu,James S Hodges
As an extension of pairwise meta-analysis of two treatments, network meta-analysis has recently attracted many researchers in evidence-based medicine because it simultaneously synthesizes both direct and indirect evidence from multiple treatments and thus facilitates better decision making. The Bayesian hierarchical model is a popular method to implement network meta-analysis, and it is generally considered
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Bayesian meta-regression model using heavy-tailed random-effects with missing sample sizes for self-thinning meta-data Stat. Interface (IF 0.412) Pub Date : 2020-07-31 Zhihua Ma; Ming-Hui Chen; Yi Tang
Motivated by the self-thinning meta-data, a randomeffects meta-analysis model with unknown precision parameters is proposed with a truncated Poisson regression model for missing sample sizes. The random effects are assumed to follow a heavy-tailed distribution to accommodate outlying aggregate values in the response variable. The logarithm of the pseudo-marginal likelihood (LPML) is used for model
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Statistical methods for quantifying between-study heterogeneity in meta-analysis with focus on rare binary events Stat. Interface (IF 0.412) Pub Date : 2020-07-31 Chiyu Zhang; Min Chen; Xinlei Wang
Meta-analysis, the statistical procedure for combining results from multiple independent studies, has been widely used in medical research to evaluate intervention efficacy and drug safety. In many practical situations, treatment effects vary notably among the collected studies, and the variation, often modeled by the between-study variance parameter $\tau^2$, can greatly affect the inference of the
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Meta-analysis of peptides to detect protein significance Stat. Interface (IF 0.412) Pub Date : 2020-07-31 Yuping Zhang; Zhengqing Ouyang; Wei-Jun Qian; Richard D. Smith; Wing Hung Wong; Ronald W. Davis
Shotgun assays are widely used in biotechnologies to characterize large molecules, which are hard to be measured as a whole directly. For instance, in Liquid Chromatography–Mass Spectrometry (LC–MS) shotgun experiments, proteins in biological samples are digested into peptides, and then peptides are separated and measured. However, in proteomics study, investigators are usually interested in the performance
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Small-study effects: current practice and challenges for future research Stat. Interface (IF 0.412) Pub Date : 2020-07-31 Arielle Marks-Anglin; Yong Chen
Meta-analyses and systematic reviews are highly valued as evidence for clinical decision- and policy-making. However, inference in these settings may be invalid if the studies do not come from the same underlying distribution. Small study effects is one form of heterogeneity that can lead to biased estimates, particularly if it arises due to the selective publishing of studies, a phenomenon known as
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Bayesian flexible hierarchical skew heavy-tailed multivariate meta regression models for individual patient data with applications. Stat. Interface (IF 0.412) Pub Date : 2020-07-31 Sungduk Kim,Ming-Hui Chen,Joseph Ibrahim,Arvind Shah,Jianxin Lin
A flexible class of multivariate meta-regression models are proposed for Individual Patient Data (IPD). The methodology is well motivated from 26 pivotal Merck clinical trials that compare statins (cholesterol lowering drugs) in combination with ezetimibe and statins alone on treatment-naïve patients and those continuing on statins at baseline. The research goal is to jointly analyze the multivariate
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Model selection between the fixed-effects model and the random-effects model in meta-analysis Stat. Interface (IF 0.412) Pub Date : 2020-07-31 Ke Yang; Hiu-Yee Kwan; Zhiling Yu; Tiejun Tong
The common-effect model and the random-effects model are the two most popular models for meta-analysis in the literature. To choose a proper model between them, the $Q$ statistic and the $I^2$ statistic are commonly used as the criteria. Recently, it is recognized that the fixed-effects model is also essential for meta-analysis, especially when the number of studies is small. With this new model, the
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Sample size estimation for future studies using Bayesian multivariate network meta-analysis Stat. Interface (IF 0.412) Pub Date : 2020-07-31 Stacia M. Desantis; Hyunsoo Hwang
Although systematic reviews of randomized clinical trials (RCTs) are considered the pinnacle of evidence-based medicine, RCTs are often designed to reach a desired level of power for a pre-specified effect size, independent of the current body of evidence. Evidence indicates that sample size calculations for a new RCT should be conducted in the context of a systematic review and meta-analysis of the
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Estimating the mean and variance from the five-number summary of a log-normal distribution Stat. Interface (IF 0.412) Pub Date : 2020-07-31 Jiandong Shi; Tiejun Tong; Yuedong Wang; Marc G. Genton
In the past several decades, meta-analysis has been widely used to pool multiple studies for evidence-based practice. To conduct a meta-analysis, the mean and variance from each study are often required; whereas in certain studies, the five-number summary may instead be reported that consists of the median, the first and third quartiles, and/or the minimum and maximum values. To transform the fivenumber
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A CD-based mapping method for combining multiple related parameters from heterogeneous intervention trials. Stat. Interface (IF 0.412) Pub Date : 2020-07-31 Yang Jiao,Eun-Young Mun,Thomas A Trikalinos,Minge Xie
Effect size can differ as a function of the elapsed time since treatment or as a function of other key covariates, such as sex or age. In evidence synthesis, a better understanding of the precise conditions under which treatment does work or does not work well has been highly valued. With increasingly accessible individual patient or participant data (IPD), more precise and informative inference can
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Fully Bayesian $L_{1/2}$-penalized linear quantile regression analysis with autoregressive errors Stat. Interface (IF 0.412) Pub Date : 2020-04-22 Yuzhu Tian; Xinyuan Song
In the quantile regression framework, we incorporate Bayesian $L_{1/2}$ and adaptive $L_{1/2}$ penalties into quantile linear regression models with autoregressive (AR) errors to conduct statistical inference. A Bayesian joint hierarchical model is established using the working likelihood of the asymmetric Laplace distribution (ALD). On the basis of the mixture representations of ALD and the generalized
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Evaluation of bias for outcome adaptive randomization designs with binary endpoints Stat. Interface (IF 0.412) Pub Date : 2020-04-22 Yaping Wang; Hongjian Zhu; J. Jack Lee
Clinical trial designs applying outcome adaptive randomization (OAR) sequentially change randomization probabilities basing on observed outcomes. Compared to the conventional equal randomization procedure, OAR has the feature to assign more patients to the better treatment arm and yield higher overall response rates for patients in the trial. However, the true response rates tend to be underestimated
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Computerized adaptive test using raw responses for item selection: Theoretical results and applications for the up-and-down method Stat. Interface (IF 0.412) Pub Date : 2020-04-22 Cheng-Der Fuh; Edward Haksing Ip; Shyh-Huei Chen
Modern computerized adaptive testing (CAT) is finding applications that contain more intensive assessments, collected over nontraditional devices such as tablets and smartphones. In this paper, we introduce an CAT algorithm that uses raw responses to adaptively select items and does not require updating the ability estimate at every administration of an item. The proposed algorithm is especially useful
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A log Birnbaum–Saunders regression model based on the skew-normal distribution under the centred parameterization Stat. Interface (IF 0.412) Pub Date : 2020-04-22 Nathalia L. Chaves; Caio L. N. Azevedo; Filidor Vilca-Labra; Juvêncio S. Nobre
In this paper we introduce a new regression model for positive and skewed data, a log Birnbaum–Saunders model based on the centred skew-normal distribution, also presenting several inference tools for this model. Initially, we developed a new version of the skew-sinh-normal distribution, describing some of its properties. For the proposed regression model, we carry out, through the expectation conditional
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Multi-dimensional classification with semiparametric mixture model Stat. Interface (IF 0.412) Pub Date : 2020-04-22 Anqi Yin; Ao Yuan
Compared to non-model based classification methods, the model based classification has the advantage of classification together with regression analysis, and is the interest of our investigation. For robustness, we propose and study a semiparametric mixture model, in which each sub-density is only assumed unimodal. The semiparametric maximum likelihood estimate is used to estimate the parametric and
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A sequential naïve Bayes method for music genre classification based on transitional information from pitch and beat Stat. Interface (IF 0.412) Pub Date : 2020-04-22 Tunan Ren; Feifei Wang; Hansheng Wang
Due to the rapid development of digital music market, online music websites are widely available in our daily life. There is a practical need to develop automatic music genre classification algorithms to manage a huge amount of music. In this regard, the transitional information contained in pitches and beats should be very useful. Particularly, the transition in pitches produces a melody, and the
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Semiparametric accelerated failure time modeling for multivariate failure times under multivariate outcome-dependent sampling designs Stat. Interface (IF 0.412) Pub Date : 2020-04-22 Tsui-Shan Lu; Sangwook Kang; Haibo Zhou
Researchers working on large cohort studies are always seeking for cost-effective designs due to a limited budget. An outcome-dependent sampling (ODS) design, a retrospective sampling scheme where one observes covariates with a probability depending on the outcome and selects supplemental samples from more informative segments, improves the study efficiency while effectively controlling for the budget
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Asymptotic theory for differentially private generalized β-models with parameters increasing Stat. Interface (IF 0.412) Pub Date : 2020-04-22 Yifan Fan; Huiming Zhang; Ting Yan
Modelling edge weights play a crucial role in the analysis of network data, which reveals the extent of relationships among individuals. Due to the diversity of weight information, sharing these data has become a complicated challenge in a privacy-preserving way. In this paper, we consider the case of the non-denoising process to achieve the trade-off between privacy and weight information in the generalized
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Estimation of a distribution function using Lagrange polynomials with Tchebychev–Gauss points Stat. Interface (IF 0.412) Pub Date : 2020-04-22 Salima Helali; Yousri Slaoui
The estimation of the distribution function of a real random variable is an intrinsic topic in non parametric estimation. To this end, a distribution estimator based on Lagrange polynomials and Tchebychev–Gauss points, is introduced. Some asymptotic properties of the proposed estimator are investigated, such as its asymptotic bias, variance, mean squared error and Chung–Smirnov propriety. The asymptotic
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A semi-parametric joint latent class model with longitudinal and survival data Stat. Interface (IF 0.412) Pub Date : 2020-04-22 Yue Liu; Ye Lin; Jianhui Zhou; Lei Liu
In many longitudinal studies, we are interested in both repeated measures of a biomarker and time to an event. When there exist heterogeneous patterns of the longitudinal and survival profiles, we propose a latent class joint model to identify subgroups of subjects and study the association between longitudinal and survival outcomes. The model is estimated by maximizing the full likelihood function
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Bayesian High-Dimensional Regression for Change Point Analysis. Stat. Interface (IF 0.412) Pub Date : 2019-09-24 Abhirup Datta,Hui Zou,Sudipto Banerjee
In many econometrics applications, the dataset under investigation spans heterogeneous regimes that are more appropriately modeled using piece-wise components for each of the data segments separated by change-points. We consider using Bayesian high-dimensional shrinkage priors in a change point setting to understand segment-specific relationship between the response and the covariates. Covariate selection
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Letter to the Editor. Stat. Interface (IF 0.412) Pub Date : 2019-08-20 Marco Geraci
Galarza, Lachos and Bandyopadhyay (2017) have recently proposed a method of estimating linear quantile mixed models (Geraci and Bottai, 2014) based on a Monte Carlo EM algorithm. They assert that their procedure represents an improvement over the numerical quadrature and non-smooth optimization approach implemented by Geraci (2014). The objective of this note is to demonstrate that this claim is incorrect
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Dynamic Structural Equation Models for Directed Cyclic Graphs: the Structural Identifiability Problem. Stat. Interface (IF 0.412) Pub Date : 2019-07-25 Yulin Wang,Yu Luo,Hulin Wu,Hongyu Miao
Network systems are commonly encountered and investigated in various disciplines, and network dynamics that refer to collective node state changes over time are one area of particular interests of many researchers. Recently, dynamic structural equation model (DSEM) has been introduced into the field of network dynamics as a powerful statistical inference tool. In this study, in recognition that parameter
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More accurate semiparametric regression in pharmacogenomics. Stat. Interface (IF 0.412) Pub Date : 2019-03-01 Yaohua Rong,Sihai Dave Zhao,Ji Zhu,Wei Yuan,Weihu Cheng,Yi Li
A key step in pharmacogenomic studies is the development of accurate prediction models for drug response based on individuals' genomic information. Recent interest has centered on semiparametric models based on kernel machine regression, which can flexibly model the complex relationships between gene expression and drug response. However, performance suffers if irrelevant covariates are unknowingly
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Bayesian modeling and uncertainty quantification for descriptive social networks. Stat. Interface (IF 0.412) Pub Date : 2019-01-22 Thomas Nemmers,Anjana Narayan,Sudipto Banerjee
This article presents a simple and easily implementable Bayesian approach to model and quantify uncertainty in small descriptive social networks. While statistical methods for analyzing networks have seen burgeoning activity over the last decade or so, ranging from social sciences to genetics, such methods usually involve sophisticated stochastic models whose estimation requires substantial structure
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Clinical Trial Design Using A Stopped Negative Binomial Distribution. Stat. Interface (IF 0.412) Pub Date : 2019-01-19 Michelle DeVeaux,Michael J Kane,Daniel Zelterman
We introduce a discrete distribution suggested by curtailed sampling rules common in early-stage clinical trials. We derive the distribution of the smallest number of independent and identically distributed Bernoulli trials needed to observe either s successes or t failures. This report provides a closed-form expression for the mass function, moment generating function, and provides connections to
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Additive Nonlinear Functional Concurrent Model. Stat. Interface (IF 0.412) Pub Date : 2018-12-05 Janet S Kim,Arnab Maity,Ana-Maria Staicu
We propose a flexible regression model to study the association between a functional response and multiple functional covariates that are observed on the same domain. Specifically, we relate the mean of the current response to current values of the covariates by a sum of smooth unknown bivariate functions, where each of the functions depends on the current value of the covariate and the time point
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Doubly regularized estimation and selection in linear mixed-effects models for high-dimensional longitudinal data. Stat. Interface (IF 0.412) Pub Date : 2018-12-05 Yun Li,Sijian Wang,Peter X-K Song,Naisyin Wang,Ling Zhou,Ji Zhu
The linear mixed-effects model (LMM) is widely used in the analysis of clustered or longitudinal data. This paper aims to address analytic challenges arising from estimation and selection in the application of the LMM to high-dimensional longitudinal data. We develop a doubly regularized approach in the LMM to simultaneously select fixed and random effects. On the theoretical front, we establish large
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Double Sparsity Kernel Learning with Automatic Variable Selection and Data Extraction. Stat. Interface (IF 0.412) Pub Date : 2018-10-09 Jingxiang Chen,Chong Zhang,Michael R Kosorok,Yufeng Liu
Learning in the Reproducing Kernel Hilbert Space (RKHS) has been widely used in many scientific disciplines. Because a RKHS can be very flexible, it is common to impose a regularization term in the optimization to prevent overfitting. Standard RKHS learning employs the squared norm penalty of the learning function. Despite its success, many challenges remain. In particular, one cannot directly use
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Time-varying copula models for longitudinal data. Stat. Interface (IF 0.412) Pub Date : 2018-04-25 Esra Kürüm,John Hughes,Runze Li,Saul Shiffman
We propose a copula-based joint modeling framework for mixed longitudinal responses. Our approach permits all model parameters to vary with time, and thus will enable researchers to reveal dynamic response-predictor relationships and response-response associations. We call the new class of models TIMECOP because we model dependence using a time-varying copula. We develop a one-step estimation procedure
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Bayesian analysis of stochastic volatility-in-mean model with leverage and asymmetrically heavy-tailed error using generalized hyperbolic skew Student's t-distribution. Stat. Interface (IF 0.412) Pub Date : 2018-01-16 William L Leão,Carlos A Abanto-Valle,Ming-Hui Chen
A stochastic volatility-in-mean model with correlated errors using the generalized hyperbolic skew Student-t (GHST) distribution provides a robust alternative to the parameter estimation for daily stock returns in the absence of normality. An efficient Markov chain Monte Carlo (MCMC) sampling algorithm is developed for parameter estimation. The deviance information, the Bayesian predictive information
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Regression analysis of incomplete data from event history studies with the proportional rates model. Stat. Interface (IF 0.412) Pub Date : 2017-12-26 Guanglei Yu,Liang Zhu,Jianguo Sun,Leslie L Robison
This paper discusses regression analysis of a type of incomplete mixed data arising from event history studies with the proportional rates model. By mixed data, we mean that each study subject may be observed continuously during the whole study period, continuously over some study periods and at some time points, or only at some discrete time points. Therefore, we have combined recurrent event and
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Quantile regression in linear mixed models: a stochastic approximation EM approach. Stat. Interface (IF 0.412) Pub Date : 2017-11-07 Christian E Galarza,Victor H Lachos,Dipankar Bandyopadhyay
This paper develops a likelihood-based approach to analyze quantile regression (QR) models for continuous longitudinal data via the asymmetric Laplace distribution (ALD). Compared to the conventional mean regression approach, QR can characterize the entire conditional distribution of the outcome variable and is more robust to the presence of outliers and misspecification of the error distribution.
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LCN: a random graph mixture model for community detection in functional brain networks. Stat. Interface (IF 0.412) Pub Date : 2017-10-17 Christopher Bryant,Hongtu Zhu,Mihye Ahn,Joseph Ibrahim
The aim of this article is to develop a Bayesian random graph mixture model (RGMM) to detect the latent class network (LCN) structure of brain connectivity networks and estimate the parameters governing this structure. The use of conjugate priors for unknown parameters leads to efficient estimation, and a well-known nonidentifiability issue is avoided by a particular parameterization of the stochastic
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Genome-wide association test of multiple continuous traits using imputed SNPs. Stat. Interface (IF 0.412) Pub Date : 2017-02-22 Baolin Wu,James S Pankow
More and more large cohort studies have conducted or are conducting genome-wide association studies (GWAS) to reveal the genetic components of many complex human diseases. These large cohort studies often collected a broad array of correlated phenotypes that reflect common physiological processes. By jointly analyzing these correlated traits, we can gain more power by aggregating multiple weak effects
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Model diagnostics in reduced-rank estimation. Stat. Interface (IF 0.412) Pub Date : 2016-12-23 Kun Chen
Reduced-rank methods are very popular in high-dimensional multivariate analysis for conducting simultaneous dimension reduction and model estimation. However, the commonly-used reduced-rank methods are not robust, as the underlying reduced-rank structure can be easily distorted by only a few data outliers. Anomalies are bound to exist in big data problems, and in some applications they themselves could
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Statistical methods and computing for big data. Stat. Interface (IF 0.412) Pub Date : 2016-10-04 Chun Wang,Ming-Hui Chen,Elizabeth Schifano,Jing Wu,Jun Yan
Big data are data on a massive scale in terms of volume, intensity, and complexity that exceed the capacity of standard analytic tools. They present opportunities as well as challenges to statisticians. The role of computational statisticians in scientific discovery from big data analyses has been under-recognized even by peer statisticians. This article summarizes recent methodological and software
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Stratified Psychiatry via Convexity-Based Clustering with Applications Towards Moderator Analysis. Stat. Interface (IF 0.412) Pub Date : 2016-03-22 Thaddeus Tarpey,Eva Petkova,Liangyu Zhu
Understanding heterogeneity in phenotypical characteristics, symptoms manifestations and response to treatment of subjects with psychiatric illnesses is a continuing challenge in mental health research. A long-standing goal of medical studies is to identify groups of subjects characterized with a particular trait or quality and to distinguish them from other subjects in a clinically relevant way. This
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A modified classification tree method for personalized medicine decisions. Stat. Interface (IF 0.412) Pub Date : 2016-01-16 Wan-Min Tsai,Heping Zhang,Eugenia Buta,Stephanie O'Malley,Ralitza Gueorguieva
The tree-based methodology has been widely applied to identify predictors of health outcomes in medical studies. However, the classical tree-based approaches do not pay particular attention to treatment assignment and thus do not consider prediction in the context of treatment received. In recent years, attention has been shifting from average treatment effects to identifying moderators of treatment
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Quantile regression for censored mixed-effects models with applications to HIV studies. Stat. Interface (IF 0.412) Pub Date : 2016-01-12 Victor H Lachos,Ming-Hui Chen,Carlos A Abanto-Valle,Caio L N Azevedo
HIV RNA viral load measures are often subjected to some upper and lower detection limits depending on the quantification assays. Hence, the responses are either left or right censored. Linear/nonlinear mixed-effects models, with slight modifications to accommodate censoring, are routinely used to analyze this type of data. Usually, the inference procedures are based on normality (or elliptical distribution)
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