
Estimation and inference for covariate adjusted partially functional linear regression models Stat. Interface (IF 0.582) Pub Date : 20210708
Zhiqiang Jiang, Zhensheng Huang, Hanbing ZhuIn this paper, we introduce covariate adjusted partially functional linear regression models, in which both the response and the covariates in the nonfunctional linear component can only be observed after being distorted by some multiplicative factors. We first estimate the distorting functions by nonparametrically regressing the response variables and covariates on the distorting covariate, and then

Multidrug combination designs with experiments in silico Stat. Interface (IF 0.582) Pub Date : 20210708
Huang HengzhenIt has become evident to medical and statistical scientists treating complex diseases that satisfactory efficacy is more likely to be achieved by using combinations of drugs. Experimental design for drug combination in preclinical studies is an important stage to move new combination therapies rapidly into clinical trials. The existing design methods for preclinical studies are primarily applied

A residualbased approach for robust random forest regression Stat. Interface (IF 0.582) Pub Date : 20210708
Andrew J. Sage, Ulrike Genschel, Dan NettletonWe introduce a novel robust approach for random forest regression that is useful when the conditional distribution of the response variable, given predictor values, is contaminated. Residual analysis is used to identify unusual response values in training data, and the contributions of these values are downweighted accordingly. This approach is motivated by a robust fitting procedure first proposed

Online multiple learning with working sufficient statistics for generalized linear models in big data Stat. Interface (IF 0.582) Pub Date : 20210708
Tonglin Zhang, Baijian YangThe article proposes an online multiple learning approach to generalized linear models (GLMs) in big data. The approach relies on a new concept called working sufficient statistics (WSS), formulated under traditional iteratively reweighted least squares (IRWLS) for maximum likelihood of GLMs. Because the algorithm needs to access the entire data set multiple times, it is impossible to directly apply

Feature screening via Bergsma–Dassios sign correlation learning Stat. Interface (IF 0.582) Pub Date : 20210708
Daojiang He, Xinxin Hao, Kai Xu, Lei He, Youxin LiuRobust rank correlation screening (RRCS) procedure that is built on Kendall $\tau$, has been suggested by Li, Peng, Zhang and Zhu (2012) as a robust alternative to the sure independence screening (SIS) method that is based on the Pearson’s correlation. However, as a drawback for certain applications is that $\tau$ may be zero even if there is an association between two random variables, RRCS is not

Group variable selection for recurrent event model with a diverging number of covariates Stat. Interface (IF 0.582) Pub Date : 20210708
Kaida Cai, Hua Shen, Xuewen LuFor the highdimensional data, the number of covariates can be large and diverge with the sample size. In this work, we propose an adaptive bilevel penalized method to solve the group variable selection problem for the recurrent event model with a diverging number of covariates. Comparing with the classical group variable selection methods, the adaptive bilevel penalized method can select the important

Regularized multiple mediation analysis Stat. Interface (IF 0.582) Pub Date : 20210708
Bin Li, Qingzhao Yu, Lu Zhang, Meichin HsiehMediation analysis is used to explore how an established exposureoutcome relationship is influenced by a third variable (mediator). Multiple mediation analysis refers to the mediation analysis with multiple mediators. We propose to use the elastic net regularized linear regression in multiple mediation analysis when the number of potential mediators is large. In exploring the exposuremediatoroutcome

Prior conditioned on scale parameter for Bayesian quantile LASSO and its generalizations Stat. Interface (IF 0.582) Pub Date : 20210708
Zhongheng Cai, Dongchu SunSeveral undesirable issues exist in the Bayesian quantile LASSO and its two generalizations, quantile group LASSO and bridge quantile regression (Alhamzawi et al. [1]; Alhamzawi and Algamal [2]; Li et al. [21]). In this paper, we numerically show that, the joint posterior may be multimodal using unconditional prior for the regression coefficients and the posterior estimates may be sensitive to the

Constrained estimation in Cox model under failuretime outcomedependent sampling design Stat. Interface (IF 0.582) Pub Date : 20210708
Jie Yin, Changming Yang, Jieli Ding, Yanyan LiuThe failuretime outcomedependent sampling (ODS) design is a costeffective sampling scheme, which can improve the efficiency of the studies by selectively including certain failures to enrich the observed sample. In modeling process, taking some prior constraints on parameters into account may lead to more powerful and efficient inferences. In this paper, we study how to fit the proportional hazards

Demystify Lindley’s paradox by connecting $p$value and posterior probability Stat. Interface (IF 0.582) Pub Date : 20210708
Guosheng Yin, Haolun ShiIn the hypothesis testing framework, $p$‑value is often computed to determine whether to reject the null hypothesis or not. On the other hand, Bayesian approaches typically compute the posterior probability of the null hypothesis to evaluate its plausibility. We revisit Lindley’s paradox and demystify the conflicting results between Bayesian and frequentist hypothesis testing procedures by casting

Bayesian confidence intervals for variance of deltalognormal distribution with an application to rainfall dispersion Stat. Interface (IF 0.582) Pub Date : 20210101
Patcharee Maneerat, Suparat Niwitpong, SaAat NiwitpongFor climate studies in agriculture, rainfall records often involve data which contain zeros and highly nonzero skewness. This is mostly used in models for prediction or that use the mean for approximation. Rainfall dispersion is also important in evaluations as it can vary enormously, and it is a natural phenomenon which can lead to drought or flood. Herein, the goal of this paper is to propose a

Robust regression model for ordinal response Stat. Interface (IF 0.582) Pub Date : 20210101
Ao Yuan, Chongyang Duan, Ming T. TanOrdinal outcome data with covariates occur frequently in statistical practice including applications from biomedicine to marketing research. Most existing methods for this type of data have relied on subjectively specified models that allow order restriction. There are also some semiparametric ordinal models which are more flexible than parametric ones, with fixed link function, they are still not

Extracting scalar measures from functional data with applications to placebo response Stat. Interface (IF 0.582) Pub Date : 20210101
Thaddeus Tarpey, Eva Petkova, Adam Ciarleglio, Robert Todd OgdenIn controlled and observational studies, outcome measures are often observed longitudinally. Such data are difficult to compare among units directly because there is no natural ordering of curves. This is relevant not only in clinical trials, where typically the goal is to evaluate the relative efficacy of treatments on average, but also in the growing and increasingly important area of personalized

A test against the stratified additive hazards model Stat. Interface (IF 0.582) Pub Date : 20210101
Yanqin Feng, Xin Yuan, Shishun ZhaoStratified data are commonly encountered in practice. An interesting problem for this type of data is to test the existence of stratum effect. This paper discusses hypothesis testing of stratum effect for intervalcensored data with informative observation times under the stratified additive hazards model. We construct a test statistic for this test problem, and show that the test statistic is asymptotically

Community detection for statistical citation network by DSCORE Stat. Interface (IF 0.582) Pub Date : 20210101
Tianchen Gao, Rui Pan, Siyu Wang, Yuehan Yang, Yan ZhangWith the wide application of statistics, it is important to identify research trends and the development of statistics. In this paper, we analyze a citation network of the top 4 statistical journals from 2001 to 2018, applying the directed spectral clustering on the ratioofeigenvectors (DSCORE) method to detect the community structure of citation network. We find that statistical researchers are

Residualbased tree for clustered binary data Stat. Interface (IF 0.582) Pub Date : 20210101
Rong Xia, Christopher R. Friese, Mousumi BanerjeeTreebased methods are widely used for classification in health sciences research, where data are often clustered. In this paper, we propose a variant of the standard classification and regression tree paradigm (CART) to handle clustered binary outcomes. Using residuals from a null generalized linear mixed model as the response, we build a regression tree to partition the covariate space into rectangles

A model checking method for the additive hazards model with multivariate current status data Stat. Interface (IF 0.582) Pub Date : 20210101
Yanqin Feng, Cheng Zhang, Jieli DingThis paper presents a class of graphical and numerical techniques to check the overall fitting adequacy of the marginal additive hazards model to multivariate current status data. The proposed testing methods are based on the supremum of the stochastic processes derived from the cumulative sum of martingalebased residuals over time and covariates. The distributions of the proposed stochastic processes

Inference in a mixture additive hazards cure model Stat. Interface (IF 0.582) Pub Date : 20210101
Dongxiao Han, Haijin He, Liuquan Sun, Xinyuan Song, Wei XuWe propose a mixture additive hazards (AH) cure model for survival data with a cure fraction. The proposed model integrates a logistic regression model for the proportion of patients cured of disease and an AH model for the uncured patients. Generalized estimating equations are developed for parameter estimation, and the asymptotic properties of the resulting estimators are established. In addition

Generalized Newton–Raphson algorithm for high dimensional LASSO regression Stat. Interface (IF 0.582) Pub Date : 20210101
Yueyong Shi, Jian Huang, Yuling Jiao, Yicheng Kang, Hu ZhangThe least absolute shrinkage and selection operator (LASSO) penalized regression is a stateoftheart statistical method in high dimensional data analysis, when the number of predictors exceeds the number of observations. The commonly used Newton–Raphson algorithm is not very successful in solving the nonsmooth optimization in LASSO. In this paper, we propose a fast generalized Newton–Raphson (GNR)

Highdimensional correlation matrix estimation for Gaussian data: a Bayesian perspective Stat. Interface (IF 0.582) Pub Date : 20210101
Chaojie Wang, Xiaodan FanGaussian covariance or precision matrix estimation is a classical problem in highdimensional data analyses. For precision matrix estimation, the graphical lasso provides an efficient approach by optimizing the loglikelihood function with $L_1$norm penalty. Inspired by the success of graphical lasso, researchers pursue analogous outcomes for covariance matrix estimation. However, it suffers from

Estimation and diagnostics for partially linear censored regression models based on heavytailed distributions Stat. Interface (IF 0.582) Pub Date : 20210101
Marcela Nuñez Lemus, Victor H. Lachos, Christian E. Galarza, Larissa A. MatosIn many studies, limited or censored data are collected. This occurs, in many situations in practice, 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

A robust nonlinear mixedeffects model for COVID19 death data Stat. Interface (IF 0.582) Pub Date : 20210101
Fernanda L. Schumacher, Clécio S. Ferreira, Marcos O. Prates, Alberto Lachos, Victor H. LachosThe analysis of complex longitudinal data such as COVID19 deaths is challenging due to several inherent features: (i) Similarlyshaped profiles with different decay patterns; (ii) Unexplained variation among repeated measurements within each country, these repeated measurements may be viewed as clustered data since they are taken on the same country at roughly the same time; (iii) Skewness, outliers

Forecasting confirmed cases of the COVID19 pandemic with a migrationbased epidemiological model Stat. Interface (IF 0.582) Pub Date : 20210101
Xinyu Wang, Lu Yang, Hong Zhang, Zhouwang Yang, Catherine LiuThe unprecedented coronavirus disease 2019 (COVID19) 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 COVID19. Different from the 2003 SARS

Heterogeneity learning for SIRS model: an application to the COVID19 Stat. Interface (IF 0.582) Pub Date : 20210101
Guanyu Hu, Junxian GengWe propose a Bayesian Heterogeneity Learning approach for SusceptibleInfectedRemovalSusceptible (SIRS) model that allows underlying clustering patterns for transmission rate, recovery rate, and loss of immunity rate for the latest coronavirus (COVID19) among different regions. Our proposed method provides simultaneously inference on parameter estimation and clustering information which contains

Penalized empirical likelihood for highdimensional generalized linear models Stat. Interface (IF 0.582) Pub Date : 20210101
Xia Chen, Liyue MaoWe develop penalized empirical likelihood for parameter estimation and variable selection in highdimensional 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 chisquare distribution

Spatial regression models for bounded response variables with evaluation of the degree of dependence Stat. Interface (IF 0.582) Pub Date : 20210101
Sandra E. Flores, Marcos O. Prates, Jorge L. Bazán, Heleno B. BolfarineBounded 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

Relating parameters in conditional, marginalized, and marginal logistic models when the mediator is binary Stat. Interface (IF 0.582) Pub Date : 20210101
Kai WangStanghellini 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

Information diffusion with network structures Stat. Interface (IF 0.582) Pub Date : 20210101
Zhu Xuening, Rui Pan, Yuxuan Zhang, Yu Chen, Wenquan Mi, Hansheng WangInformation 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

Lead time distribution for individuals with a screening history Stat. Interface (IF 0.582) Pub Date : 20210101
Ruiqi Liu, Dongfeng Wu, Shesh N. RaiWe 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 subPDF. 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

Bayesian zeroinflated growth mixture models with application to health risk behavior data Stat. Interface (IF 0.582) Pub Date : 20210101
Si Yang, Gavino PuggioniThis paper focuses on developing latent class models for longitudinal data with zeroinflated count response variables. The goals are to model discrete longitudinal patterns of rare events counts (for instance, healthrisky behavior), and to identify individualspecific covariates associated with latent class probabilities. Two discrete latent structures are present in this type of model: a latent

Estimating equation estimators of quantile differences for one sample with lengthbiased and rightcensored data Stat. Interface (IF 0.582) Pub Date : 20210101
Dehui Wang, Li Xun, Guangchao Zhang, Yong ZhouThis paper estimates quantile differences for one sample with lengthbiased and rightcensored (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 kernelsmoothed approach is employed. To make full use of the features of LBRC data, the augmented

A nonmarginal variable screening method for the varying coefficient Cox model Stat. Interface (IF 0.582) Pub Date : 20210101
Lianqiang Qu, Liuquan SunThe 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 ultrahighdimensional settings. In addition, an iterative

Grouped variable selection with prior information via the prior group bridge method Stat. Interface (IF 0.582) Pub Date : 20210101
Kai Li, Meng Mei, Yuan JiangIn 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 socalled “bilevel selection,” group bridge has been developed as a combination of grouplevel bridge and variablelevel lasso penalties. However, in many scientific areas, prior knowledge is available about the importance

The timing and effectiveness of implementing mild interventions of COVID19 in large industrial regions via a synthetic control method Stat. Interface (IF 0.582) Pub Date : 20210101
Ting Tian, Wenxiang Luo, Jianbin Tan, Yukang Jiang, Minqiong Chen, Wenliang Pan, Songpan Yang, Jiashu Zhao, Xueqin Wang, Heping ZhangThe outbreak of novel coronavirus disease (COVID19) 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 COVID19 in Shenzhen, China is valuable because populated industrial cities are the epic centres of COVID19 in many regions. We made use of synthetic control methods to create a

Discussion on “The timing and effectiveness of implementing mild interventions of COVID19 in large industrial regions via a synthetic control method” by Tian et al. Stat. Interface (IF 0.582) Pub Date : 20210101
Kun Chen, Fei WangThe ongoing pandemic of the novel coronavirus disease 2019 (COVID19) has impacted tens of millions of people and caused a huge economic loss. Most of the impacted countries have implemented different nonpharmaceutical interventions (NPIs) to control and prevent the spreading of SARSCov2, which is the virus causing COVID19. With the coming flu season in the northern hemisphere, many countries are

Discussion on “The timing and effectiveness of implementing mild interventions of COVID19 in large industrial regions via a synthetic control method” by Tian et al. Stat. Interface (IF 0.582) Pub Date : 20210101
Yifan ZhuThis article provides an overview and discussion of the recent published paper from Tian et al. on modeling the differences of COVID19 outbreak between Shenzhen and a synthetic population constructed from 68 US counties.

Discussion on “The timing and effectiveness of implementing mild interventions of COVID19 in large industrial regions via a synthetic control method” by Tian et al. Stat. Interface (IF 0.582) Pub Date : 20210101
Soumik Purkayastha, Peter SongIt 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 COVID19 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

Discussion on “The timing and effectiveness of implementing mild interventions of COVID19 in large industrial regions via a synthetic control method” by Tian et al. Stat. Interface (IF 0.582) Pub Date : 20210101
Debashree Ray, Rupam Bhattacharyya, Bhramar MukherjeeTian 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 COVID19 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

The need to incorporate communities in compartmental models Stat. Interface (IF 0.582) Pub Date : 20210101
Michael J. Kane, Owais GilaniTian et al. provide a framework for assessing populationlevel interventions of disease outbreaks through the construction of counterfactuals in a largescale, natural experiment assessing the efficacy of mild, but early interventions compared to delayed interventions. The technique is applied to the recent SARSCoV2 outbreak with the population of Shenzhen, China acting as the mildbutearly treatment

A novel intervention recurrent autoencoder for real time forecasting and nonpharmaceutical intervention selection to curb the spread of Covid19 in the world Stat. Interface (IF 0.582) Pub Date : 20210101
Qiyang Ge, Zixin Hu, Shudi Li, Wei Lin, Li Jin, Momiao XiongAs the Covid19 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 Covid19. Widely used statistical and computer methods for modeling and forecasting the trajectory of Covid19 are epidemiological models. Although these epidemiological

Computerized adaptive test using raw responses for item selection: Theoretical results and applications for the upanddown method Stat. Interface (IF 0.582) Pub Date : 20200422
ChengDer Fuh, Edward Haksing Ip, ShyhHuei ChenModern 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

Multidimensional classification with semiparametric mixture model Stat. Interface (IF 0.582) Pub Date : 20200422
Anqi Yin, Ao YuanCompared to nonmodel 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 subdensity is only assumed unimodal. The semiparametric maximum likelihood estimate is used to estimate the parametric and

A sequential naïve Bayes method for music genre classification based on transitional information from pitch and beat Stat. Interface (IF 0.582) Pub Date : 20200422
Tunan Ren, Feifei Wang, Hansheng WangDue 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

A spatial autoregression model with timevarying coefficients Stat. Interface (IF 0.582) Pub Date : 20200101
Ke Xu, Luping Sun, Jin Liu, Xuening Zhu, Hansheng WangThis article proposes a spatial autoregression (SAR) model with timevarying coefficients. The model incorporates both spatial dependence and the impacts of explanatory variables, and all the coefficients are allowed to flexibly vary according to time. This article further develops a kernelsmoothed estimator (KSE) to estimate the timevarying coefficients. Compared with the maximum likelihood estimator

Additive hazards regression for casecohort studies with intervalcensored data Stat. Interface (IF 0.582) Pub Date : 20200101
Mingyue Du, Huiqiong Li, Jianguo SunA large literature has been developed for the analysis of casecohort studies that are often performed with the aim of reducing the cost on the collection of covariate information. In particular, many authors have discussed their regression analysis under the framework of the additive hazards model, which is often preferred when the risk difference is of main interest. However, all of the existing

Nonparametric statistical inference and imputation for incomplete categorical data Stat. Interface (IF 0.582) Pub Date : 20200101
Chaojie Wang, Linghao Shen, Han Li, Xiaodan FanMissingness in categorical data is a common problem in various real applications. Traditional approaches either utilize only the complete observations or impute the missing data by some ad hoc methods rather than the true conditional distribution of the missing data, thus losing or distorting the rich information in the partial observations. In this paper, we propose the Dirichlet Process Mixture of

NonGaussian stochastic volatility model with jumps via Gibbs sampler Stat. Interface (IF 0.582) Pub Date : 20200101
Arthur T. Rego, Thiago R. dos SantosIn this work, we propose a model for estimating volatility from financial time series, extending the nonGaussian family of spacestate models with exact marginal likelihood proposed by Gamerman, Santos and Franco (2013). On the literature there are models focused on estimating financial assets risk, however, most of them rely on MCMC methods based on Metropolis algorithms, since full conditional posterior

Copula modeling for data with ties Stat. Interface (IF 0.582) Pub Date : 20200101
Yan Li, Yang Li, Yichen Qin, Jun YanCopula modeling has gained much attention in many fields recently with the advantage of separating dependence structure from marginal distributions. In real data, however, serious ties are often present in one or multiple margins, which cause problems to many rankbased statistical methods developed under the assumption of continuous data with no ties. Simple methods such as breaking the ties at random

Highdimensional twosample precision matrices test: an adaptive approach through multiplier bootstrap Stat. Interface (IF 0.582) Pub Date : 20200101
Mingjuan Zhang, Yong He, Cheng Zhou, Xinsheng ZhangPrecision matrix, which is the inverse of covariance matrix, plays an important role in statistics, as it captures the partial correlation between variables. Testing the equality of two precision matrices in high dimensional setting is a very challenging but meaningful problem, especially in the differential network modelling. To our best knowledge, existing test is only powerful for sparse alternative

Zerooneinflated simplex regression models for the analysis of continuous proportion data Stat. Interface (IF 0.582) Pub Date : 20200101
Pengyi Liu, Kam Chuen Yuen, LiuCang Wu, GuoLiang Tian, Tao LiContinuous data restricted in the closed unit interval [0,1] often appear in various fields. Neither the beta distribution nor the simplex distribution provides a satisfactory fitting for such data, since the densities of the two distributions are defined only in the open interval (0,1). To model continuous proportional data with excessive zeros and excessive ones, it is the first time that we propose

A composite nonparametric product limit approach for estimating the distribution of survival times under lengthbiased and rightcensored data Stat. Interface (IF 0.582) Pub Date : 20200101
Shuqin Fan, Wei Zhao, Alan T. K. Wan, Yong ZhouThis paper considers a composite nonparametric product limit estimator for estimating the distribution of survival times when the data are lengthbiased and right censored. Our method takes into account auxiliary information that frequently arises in survival analysis, and is easier to implement than existing methods for estimating survival functions. We derive a strong representation of the proposed

A test on linear hypothesis of $k$sample means in highdimensional data Stat. Interface (IF 0.582) Pub Date : 20200101
Mingxiang Cao, Peng Sun, Daojiang He, Rui Wang, Xingzhong XuIn this paper, a new test procedure is proposed to test a linear hypothesis of ksample mean vectors in highdimensional normal models with heteroskedasticity. The motivation is on the basis of the generalized likelihood ratio method and the Bennett transformation. The asymptotic distributions of the new test are derived under null and local alternative hypotheses under mild conditions. Simulation results

Adaptive LASSO regression against heteroscedastic idiosyncratic factors in the covariates Stat. Interface (IF 0.582) Pub Date : 20200101
Kaimeng Zhang, Chi Tim NgRecent studies suggest that by including the principal components of the covariates, LASSO regression achieves certain consistency properties when the idiosyncratic factors are homoscedastic. In this paper, it is shown that if the principal components are replaced by the common factors obtained based on the maximum likelihood estimation of factor model and the covariates are replaced by the estimated

Bayesian kernel adaptive grouping learning for multidimensional datasets Stat. Interface (IF 0.582) Pub Date : 20200101
Xiaozhou Wang, Fangli DongWith the development of information technology, a large number of datasets with complex structures, such as multidimensional datasets, need to be processed and analyzed. In this paper we propose a kernelbased statistical learning algorithm, Bayesian Kernel Adaptive Grouping Learning (BKAGL), to provide an innovative solution for the classification problem of multidimensional datasets. BKAGL can integrate

On evidence cycles in network metaanalysis Stat. Interface (IF 0.582) Pub Date : 20200101
Lifeng Lin, Haitao Chu, James S. HodgesAs an extension of pairwise metaanalysis of two treatments, network metaanalysis has recently attracted many researchers in evidencebased 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 metaanalysis, and it is generally considered

Bayesian MetaRegression Model Using HeavyTailed Randomeffects with Missing Sample Sizes for Selfthinning Metadata Stat. Interface (IF 0.582) Pub Date : 20200101
Zhihua Ma, MingHui Chen, Yi TangMotivated by the selfthinning metadata, a randomeffects metaanalysis 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 heavytailed distribution to accommodate outlying aggregate values in the response variable. The logarithm of the pseudomarginal likelihood (LPML) is used for model

Statistical Methods for Quantifying Betweenstudy Heterogeneity in Metaanalysis with Focus on Rare Binary Events Stat. Interface (IF 0.582) Pub Date : 20200101
Chiyu Zhang, Min Chen, Xinlei WangMetaanalysis, 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 betweenstudy variance parameter τ, can greatly affect the inference of the overall

Metaanalysis of peptides to detect protein significance Stat. Interface (IF 0.582) Pub Date : 20200101
Yuping Zhang, Zhengqing Ouyang, WeiJun Qian, Richard D. Smith, Wing Hung Wong, Ronald W. DavisShotgun 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 (LCMS) 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

Smallstudy effects: current practice and challenges for future research Stat. Interface (IF 0.582) Pub Date : 20200101
Arielle MarksAnglin, Yong ChenMetaanalyses and systematic reviews are highly valued as evidence for clinical decisionand policymaking. 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

Bayesian Flexible Hierarchical Skew HeavyTailed Multivariate Meta Regression Models for Individual Patient Data with Applications Stat. Interface (IF 0.582) Pub Date : 20200101
Sungduk Kim, MingHui Chen, Joseph Ibrahim, Arvind Shah, Jianxin LinA flexible class of multivariate metaregression 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 treatmentnaïve patients and those continuing on statins at baseline. The research goal is to jointly analyze the multivariate
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