In this paper, we introduce covariate adjusted partially functional linear regression models, in which both the response and the covariates in the non-functional 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

It 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 pre-clinical studies is an important stage to move new combination therapies rapidly into clinical trials. The existing design methods for pre-clinical studies are primarily applied

We 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 down-weighted accordingly. This approach is motivated by a robust fitting procedure first proposed

The 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

Robust 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

For the high-dimensional data, the number of covariates can be large and diverge with the sample size. In this work, we propose an adaptive bi-level 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 bi-level penalized method can select the important

Mediation analysis is used to explore how an established exposure-outcome 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 exposure-mediator-outcome

Several 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

The failure-time outcome-dependent sampling (ODS) design is a cost-effective 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

In 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

For climate studies in agriculture, rainfall records often involve data which contain zeros and highly non-zero 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

Ordinal 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

In 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

Stratified 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 interval-censored 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

With 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 ratio-of-eigenvectors (D-SCORE) method to detect the community structure of citation network. We find that statistical researchers are

Tree-based 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

This 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 martingale-based residuals over time and covariates. The distributions of the proposed stochastic processes

We 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

The least absolute shrinkage and selection operator (LASSO) penalized regression is a state-of-the-art 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 non-smooth optimization in LASSO. In this paper, we propose a fast generalized Newton–Raphson (GNR)

Gaussian covariance or precision matrix estimation is a classical problem in high-dimensional data analyses. For precision matrix estimation, the graphical lasso provides an efficient approach by optimizing the log-likelihood 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

In 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

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, 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

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 coronavirus (COVID-19) among different regions. Our proposed method provides simultaneously inference on parameter estimation and clustering information which contains

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

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

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

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

This article proposes a spatial autoregression (SAR) model with time-varying 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 kernel-smoothed estimator (KSE) to estimate the timevarying coefficients. Compared with the maximum likelihood estimator

A large literature has been developed for the analysis of case-cohort 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

Missingness 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

In this work, we propose a model for estimating volatility from financial time series, extending the non-Gaussian family of space-state 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 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 rank-based statistical methods developed under the assumption of continuous data with no ties. Simple methods such as breaking the ties at random

Precision 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

Continuous 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

This paper considers a composite nonparametric product limit estimator for estimating the distribution of survival times when the data are length-biased 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

In this paper, a new test procedure is proposed to test a linear hypothesis of k-sample 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

Recent 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

With 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 kernel-based statistical learning algorithm, Bayesian Kernel Adaptive Grouping Learning (BKAGL), to provide an innovative solution for the classification problem of multi-dimensional datasets. BKAGL can integrate

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 τ, can greatly affect the inference of the overall

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

Meta-analyses and systematic reviews are highly valued as evidence for clinical decisionand 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