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

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

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

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

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

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

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

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

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

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

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

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

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.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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.

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

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

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

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

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

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

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)