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  • Identification and inference with nonignorable missing covariate data.
    Stat. Sin. (IF 0.968) Pub Date : 2018-10-1
    Wang Miao; Eric Tchetgen Tchetgen

    We study identification of parametric and semiparametric models with missing covariate data. When covariate data are missing not at random, identification is not guaranteed even under fairly restrictive parametric assumptions, a fact that is illustrated with several examples. We propose a general approach to establish identification of parametric and semiparametric models when a covariate is missing

    更新日期:2020-12-23
  • Semiparametric Estimation with Data Missing Not at Random Using an Instrumental Variable.
    Stat. Sin. (IF 0.968) Pub Date : 2018-10-1
    BaoLuo Sun; Lan Liu; Wang Miao; Kathleen Wirth; James Robins; Eric J Tchetgen Tchetgen

    Missing data occur frequently in empirical studies in health and social sciences, often compromising our ability to make accurate inferences. An outcome is said to be missing not at random (MNAR) if, conditional on the observed variables, the missing data mechanism still depends on the unobserved outcome. In such settings, identification is generally not possible without imposing additional assumptions

    更新日期:2020-12-20
  • Multicategory Outcome Weighted Margin-based Learning for Estimating Individualized Treatment Rules.
    Stat. Sin. (IF 0.968) Pub Date : 2020-12-15
    Chong Zhang; Jingxiang Chen; Haoda Fu; Xuanyao He; Ying-Qi Zhao; Yufeng Liu

    Due to heterogeneity for many chronic diseases, precise personalized medicine, also known as precision medicine, has drawn increasing attentions in the scientific community. One main goal of precision medicine is to develop the most effective tailored therapy for each individual patient. To that end, one needs to incorporate individual characteristics to detect a proper individual treatment rule (ITR)

    更新日期:2020-12-16
  • Spatial Factor Models for High-Dimensional and Large Spatial Data: An Application in Forest Variable Mapping.
    Stat. Sin. (IF 0.968) Pub Date : 2019-1-1
    Daniel Taylor-Rodriguez; Andrew O Finley; Abhirup Datta; Chad Babcock; Hans-Erik Andersen; Bruce D Cook; Douglas C Morton; Sudipto Banerjee

    Gathering information about forest variables is an expensive and arduous activity. As such, directly collecting the data required to produce high-resolution maps over large spatial domains is infeasible. Next generation collection initiatives of remotely sensed Light Detection and Ranging (LiDAR) data are specifically aimed at producing complete-coverage maps over large spatial domains. Given that

    更新日期:2020-12-16
  • IDENTIFICATION AND INFERENCE FOR MARGINAL AVERAGE TREATMENT EFFECT ON THE TREATED WITH AN INSTRUMENTAL VARIABLE.
    Stat. Sin. (IF 0.968) Pub Date : 2020-11-20
    Lan Liu; Wang Miao; Baoluo Sun; James Robins; Eric Tchetgen Tchetgen

    In observational studies, treatments are typically not randomized and therefore estimated treatment effects may be subject to confounding bias. The instrumental variable (IV) design plays the role of a quasi-experimental handle since the IV is associated with the treatment and only affects the outcome through the treatment. In this paper, we present a novel framework for identification and inference

    更新日期:2020-11-21
  • Feature Screening in Ultrahigh Dimensional Generalized Varying-coefficient Models.
    Stat. Sin. (IF 0.968) Pub Date : 2020-9-29
    Guangren Yang; Songshan Yang; Runze Li

    Generalized varying coefficient models are particularly useful for examining dynamic effects of covariates on a continuous, binary or count response. This paper is concerned with feature screening for generalized varying coefficient models with ultrahigh dimensional covariates. The proposed screening procedure is based on joint quasi-likelihood of all predictors, and therefore is distinguished from

    更新日期:2020-09-30
  • A mean score method for sensitivity analysis to departures from the missing at random assumption in randomised trials.
    Stat. Sin. (IF 0.968) Pub Date : 2018-10-5
    Ian R White,James Carpenter,Nicholas J Horton

    Most analyses of randomised trials with incomplete outcomes make untestable assumptions and should therefore be subjected to sensitivity analyses. However, methods for sensitivity analyses are not widely used. We propose a mean score approach for exploring global sensitivity to departures from missing at random or other assumptions about incomplete outcome data in a randomised trial. We assume a single

    更新日期:2020-09-28
  • ON ESTIMATION OF THE OPTIMAL TREATMENT REGIME WITH THE ADDITIVE HAZARDS MODEL.
    Stat. Sin. (IF 0.968) Pub Date : 2018-8-24
    Suhyun Kang,Wenbin Lu,Jiajia Zhang

    We propose a doubly robust estimation method for the optimal treatment regime based on an additive hazards model with censored survival data. Specifically, we introduce a new semiparametric additive hazard model which allows flexible baseline covariate effects in the control group and incorporates marginal treatment effect and its linear interaction with covariates. In addition, we propose a time-dependent

    更新日期:2020-09-28
  • Partial Consistency with Sparse Incidental Parameters.
    Stat. Sin. (IF 0.968) Pub Date : 2018-5-1
    Jianqing Fan,Runlong Tang,Xiaofeng Shi

    Penalized estimation principle is fundamental to high-dimensional problems. In the literature, it has been extensively and successfully applied to various models with only structural parameters. As a contrast, in this paper, we first apply this penalization principle to a linear regression model with a finite-dimensional vector of structural parameters and a high-dimensional vector of sparse incidental

    更新日期:2020-09-28
  • Scalable Bayesian Variable Selection Using Nonlocal Prior Densities in Ultrahigh-dimensional Settings.
    Stat. Sin. (IF 0.968) Pub Date : 2018-4-13
    Minsuk Shin,Anirban Bhattacharya,Valen E Johnson

    Bayesian model selection procedures based on nonlocal alternative prior densities are extended to ultrahigh dimensional settings and compared to other variable selection procedures using precision-recall curves. Variable selection procedures included in these comparisons include methods based on g-priors, reciprocal lasso, adaptive lasso, scad, and minimax concave penalty criteria. The use of precision-recall

    更新日期:2020-09-28
  • Two-Sample Tests for High-Dimensional Linear Regression with an Application to Detecting Interactions.
    Stat. Sin. (IF 0.968) Pub Date : 2018-2-2
    Yin Xia,Tianxi Cai,T Tony Cai

    Motivated by applications in genomics, we consider in this paper global and multiple testing for the comparisons of two high-dimensional linear regression models. A procedure for testing the equality of the two regression vectors globally is proposed and shown to be particularly powerful against sparse alternatives. We then introduce a multiple testing procedure for identifying unequal coordinates

    更新日期:2020-09-28
  • Predicting disease Risk by Transformation Models in the Presence of Unspecified Subgroup Membership.
    Stat. Sin. (IF 0.968) Pub Date : 2017-11-4
    Qianqian Wang,Yanyuan Ma,Yuanjia Wang

    Some biomedical studies lead to mixture data. When a discrete covariate defining subgroup membership is missing for some of the subjects in a study, the distribution of the outcome follows a mixture distribution of the subgroup-specific distributions. Taking into account the uncertain distribution of the group membership and the covariates, we model the relation between the disease onset time and the

    更新日期:2020-09-28
  • SEMIPARAMETRIC REGRESSION ANALYSIS OF REPEATED CURRENT STATUS DATA.
    Stat. Sin. (IF 0.968) Pub Date : 2017-9-30
    Baosheng Liang,Xingwei Tong,Donglin Zeng,Yuanjia Wang

    In many clinical studies, patients may be asked to report their medication adherence, presence of side effects, substance use, and hospitalization information during the study period. However, the exact occurrence time of these recurrent events may not be available due to privacy protection, recall difficulty, or incomplete medical records. Instead, the only available information is whether the events

    更新日期:2020-09-28
  • Variable Selection via Partial Correlation.
    Stat. Sin. (IF 0.968) Pub Date : 2017-7-1
    Runze Li,Jingyuan Liu,Lejia Lou

    Partial correlation based variable selection method was proposed for normal linear regression models by Bühlmann, Kalisch and Maathuis (2010) as a comparable alternative method to regularization methods for variable selection. This paper addresses two important issues related to partial correlation based variable selection method: (a) whether this method is sensitive to normality assumption, and (b)

    更新日期:2020-09-28
  • CONTROL FUNCTION ASSISTED IPW ESTIMATION WITH A SECONDARY OUTCOME IN CASE-CONTROL STUDIES.
    Stat. Sin. (IF 0.968) Pub Date : 2017-6-27
    Tamar Sofer,Marilyn C Cornelis,Peter Kraft,Eric J Tchetgen Tchetgen

    Case-control studies are designed towards studying associations between risk factors and a single, primary outcome. Information about additional, secondary outcomes is also collected, but association studies targeting such secondary outcomes should account for the case-control sampling scheme, or otherwise results may be biased. Often, one uses inverse probability weighted (IPW) estimators to estimate

    更新日期:2020-09-28
  • Joint Estimation of Multiple High-dimensional Precision Matrices.
    Stat. Sin. (IF 0.968) Pub Date : 2017-3-21
    T Tony Cai,Hongzhe Li,Weidong Liu,Jichun Xie

    Motivated by analysis of gene expression data measured in different tissues or disease states, we consider joint estimation of multiple precision matrices to effectively utilize the partially shared graphical structures of the corresponding graphs. The procedure is based on a weighted constrained ℓ∞/ℓ1 minimization, which can be effectively implemented by a second-order cone programming. Compared to

    更新日期:2020-09-28
  • The Statistics and Mathematics of High Dimension Low Sample Size Asymptotics.
    Stat. Sin. (IF 0.968) Pub Date : 2016-12-27
    Dan Shen,Haipeng Shen,Hongtu Zhu,J S Marron

    The aim of this paper is to establish several deep theoretical properties of principal component analysis for multiple-component spike covariance models. Our new results reveal an asymptotic conical structure in critical sample eigendirections under the spike models with distinguishable (or indistinguishable) eigenvalues, when the sample size and/or the number of variables (or dimension) tend to infinity

    更新日期:2020-09-28
  • Marginal screening for high-dimensional predictors of survival outcomes.
    Stat. Sin. (IF 0.968) Pub Date : 2020-1-16
    Tzu-Jung Huang,Ian W McKeague,Min Qian

    This study develops a marginal screening test to detect the presence of significant predictors for a right-censored time-to-event outcome under a high-dimensional accelerated failure time (AFT) model. Establishing a rigorous screening test in this setting is challenging, because of the right censoring and the post-selection inference. In the latter case, an implicit variable selection step needs to

    更新日期:2020-09-28
  • Large-Scale Simultaneous Testing of Cross-Covariance Matrices with Applications to PheWAS.
    Stat. Sin. (IF 0.968) Pub Date : 2020-1-1
    Tianxi Cai; T Tony Cai; Katherine Liao; Weidong Liu

    Motivated by applications in phenome-wide association studies (PheWAS), we consider in this paper simultaneous testing of columns of high-dimensional cross-covariance matrices and develop a multiple testing procedure with theoretical guarantees. It is shown that the proposed testing procedure maintains a desired false discovery rate (FDR) and false discovery proportion (FDP) under mild regularity conditions

    更新日期:2020-09-28
  • FMEM: Functional Mixed Effects Models for Longitudinal Functional Responses.
    Stat. Sin. (IF 0.968) Pub Date : 2019-11-21
    Hongtu Zhu,Kehui Chen,Xinchao Luo,Ying Yuan,Jane-Ling Wang

    The aim of this paper is to conduct a systematic and theoretical analysis of estimation and inference for a class of functional mixed effects models (FMEM). Such FMEMs consist of fixed effects that characterize the association between longitudinal functional responses and covariates of interest and random effects that capture the spatial-temporal correlations of longitudinal functional responses. We

    更新日期:2020-09-28
  • SEMIPARAMETRIC TRANSFORMATION MODELS WITH MULTILEVEL RANDOM EFFECTS FOR CORRELATED DISEASE ONSET IN FAMILIES.
    Stat. Sin. (IF 0.968) Pub Date : 2019-10-4
    Baosheng Liang,Yuanjia Wang,Donglin Zeng

    Large cohort studies are commonly launched to study risk of genetic variants or other risk factors on age at onset (AAO) of a chronic disorder. In these studies, family history data including AAO of disease in family members are collected to provide additional information and can be used to improve efficiency. Statistical analysis of these data is challenging due to missing genotypes in family members

    更新日期:2020-09-28
  • Spatial Joint Species Distribution Modeling using Dirichlet Processes.
    Stat. Sin. (IF 0.968) Pub Date : 2019-9-27
    Shinichiro Shirota,Alan E Gelfand,Sudipto Banerjee

    Species distribution models usually attempt to explain presence-absence or abundance of a species at a site in terms of the environmental features (so-called abiotic features) present at the site. Historically, such models have considered species individually. However, it is well-established that species interact to influence presence-absence and abundance (envisioned as biotic factors). As a result

    更新日期:2020-09-28
  • Entropy Learning for Dynamic Treatment Regimes.
    Stat. Sin. (IF 0.968) Pub Date : 2019-9-20
    Binyan Jiang,Rui Song,Jialiang Li,Donglin Zeng

    Estimating optimal individualized treatment rules (ITRs) in single or multi-stage clinical trials is one key solution to personalized medicine and has received more and more attention in statistical community. Recent development suggests that using machine learning approaches can significantly improve the estimation over model-based methods. However, proper inference for the estimated ITRs has not

    更新日期:2020-09-28
  • NONPARAMETRIC INFERENCE FOR MARKOV PROCESSES WITH MISSING ABSORBING STATE.
    Stat. Sin. (IF 0.968) Pub Date : 2019-9-14
    Giorgos Bakoyannis,Ying Zhang,Constantin T Yiannoutsos

    This paper deals with the issue of nonparametric estimation of the transition probability matrix of a non-homogeneous Markov process with finite state space and partially observed absorbing state. We impose a missing at random assumption and propose a computationally efficient nonparametric maximum pseudolikelihood estimator (NPMPLE). The estimator depends on a parametric model that is used to estimate

    更新日期:2020-09-28
  • SEMIPARAMETRIC REGRESSION MODEL FOR RECURRENT BACTERIAL INFECTIONS AFTER HEMATOPOIETIC STEM CELL TRANSPLANTATION.
    Stat. Sin. (IF 0.968) Pub Date : 2019-9-13
    Chi Hyun Lee,Chiung-Yu Huang,Todd E DeFor,Claudio G Brunstein,Daniel J Weisdorf,Xianghua Luo

    Patients who undergo hematopoietic stem cell transplantation (HSCT) often experience multiple bacterial infections during the early post-transplant period. In this article, we consider a semiparametric regression model that correlates patient- and transplant-related risk factors with inter-infection gap times. Existing regression methods for recurrent gap times are not directly applicable to study

    更新日期:2020-09-28
  • Estimation of Area Under the ROC Curve under nonignorable verification bias.
    Stat. Sin. (IF 0.968) Pub Date : 2019-8-2
    Wenbao Yu,Jae Kwang Kim,Taesung Park

    The Area Under the Receiving Operating Characteristic Curve (AUC) is frequently used for assessing the overall accuracy of a diagnostic marker. However, estimation of AUC relies on knowledge of the true outcomes of subjects: diseased or non-diseased. Because disease verification based on a gold standard is often expensive and/or invasive, only a limited number of patients are sent to verification at

    更新日期:2020-09-28
  • Hybrid combinations of parametric and empirical likelihoods.
    Stat. Sin. (IF 0.968) Pub Date : 2019-7-3
    Nils Lid Hjort,Ian W McKeague,Ingrid Van Keilegom

    This paper develops a hybrid likelihood (HL) method based on a compromise between parametric and nonparametric likelihoods. Consider the setting of a parametric model for the distribution of an observation Y with parameter θ. Suppose there is also an estimating function m(·, μ) identifying another parameter μ via Em(Y, μ) = 0, at the outset defined independently of the parametric model. To borrow strength

    更新日期:2020-09-28
  • INFERENCE FOR LOW-DIMENSIONAL COVARIATES IN A HIGH-DIMENSIONAL ACCELERATED FAILURE TIME MODEL.
    Stat. Sin. (IF 0.968) Pub Date : 2019-5-11
    Hao Chai,Qingzhao Zhang,Jian Huang,Shuangge Ma

    Data with high-dimensional covariates are now commonly encountered. Compared to other types of responses, research on high-dimensional data with censored survival responses is still relatively limited, and most of the existing studies have been focused on estimation and variable selection. In this study, we consider data with a censored survival response, a set of low-dimensional covariates of main

    更新日期:2020-09-28
  • EDGEWORTH CORRECTION FOR THE LARGEST EIGENVALUE IN A SPIKED PCA MODEL.
    Stat. Sin. (IF 0.968) Pub Date : 2019-3-20
    Jeha Yang,Iain M Johnstone

    We study improved approximations to the distribution of the largest eigenvalue ℓ ^ of the sample covariance matrix of n zero-mean Gaussian observations in dimension p + 1. We assume that one population principal component has variance ℓ > 1 and the remaining 'noise' components have common variance 1. In the high-dimensional limit p/n → γ > 0, we study Edgeworth corrections to the limiting Gaussian

    更新日期:2020-09-28
  • SENSITIVITY ANALYSIS FOR UNMEASURED CONFOUNDING IN COARSE STRUCTURAL NESTED MEAN MODELS.
    Stat. Sin. (IF 0.968) Pub Date : 2019-3-12
    Shu Yang,Judith J Lok

    Coarse Structural Nested Mean Models (SNMMs, Robins (2000)) and G-estimation can be used to estimate the causal effect of a time-varying treatment from longitudinal observational studies. However, they rely on an untestable assumption of no unmeasured confounding. In the presence of unmeasured confounders, the unobserved potential outcomes are not missing at random, and standard G-estimation leads

    更新日期:2020-09-28
  • Smoothed Rank Regression for the Accelerated Failure Time Competing Risks Model with Missing Cause of Failure.
    Stat. Sin. (IF 0.968) Pub Date : 2019-2-12
    Zhiping Qiu,Alan T K Wan,Yong Zhou,Peter B Gilbert

    This paper examines the accelerated failure time competing risks model with missing cause of failure using the monotone class rank-based estimating equations approach. We handle the non-smoothness of the rank-based estimating equations using a kernel smoothed estimation method, and estimate the unknown selection probability and the conditional expectation by non-parametric techniques. Under this setup

    更新日期:2020-09-28
  • TENSOR GENERALIZED ESTIMATING EQUATIONS FOR LONGITUDINAL IMAGING ANALYSIS.
    Stat. Sin. (IF 0.968) Pub Date : 2019-1-1
    Xiang Zhang,Lexin Li,Hua Zhou,Yeqing Zhou,Dinggang Shen,

    Longitudinal neuroimaging studies are becoming increasingly prevalent, where brain images are collected on multiple subjects at multiple time points. Analyses of such data are scientifically important, but also challenging. Brain images are in the form of multidimensional arrays, or tensors, which are characterized by both ultrahigh dimensionality and a complex structure. Longitudinally repeated images

    更新日期:2020-09-28
  • MM ALGORITHMS FOR VARIANCE COMPONENT ESTIMATION AND SELECTION IN LOGISTIC LINEAR MIXED MODEL.
    Stat. Sin. (IF 0.968) Pub Date : 2019-1-1
    Liuyi Hu,Wenbin Lu,Jin Zhou,Hua Zhou

    Logistic linear mixed models are widely used in experimental designs and genetic analyses of binary traits. Motivated by modern applications, we consider the case of many groups of random effects, where each group corresponds to a variance component. When the number of variance components is large, fitting a logistic linear mixed model is challenging. Thus, we develop two efficient and stable mino

    更新日期:2020-09-28
  • Bayesian Modeling and Inference for Nonignorably Missing Longitudinal Binary Response Data with Applications to HIV Prevention Trials.
    Stat. Sin. (IF 0.968) Pub Date : 2019-1-1
    Jing Wu,Joseph G Ibrahim,Ming-Hui Chen,Elizabeth D Schifano,Jeffrey D Fisher

    Missing data are frequently encountered in longitudinal clinical trials. To better monitor and understand the progress over time, one must handle the missing data appropriately and examine whether the missing data mechanism is ignorable or nonignorable. In this article, we develop a new probit model for longitudinal binary response data. It resolves a challenging issue for estimating the variance of

    更新日期:2020-09-28
  • Functional Linear Regression Models for Nonignorable Missing Scalar Responses.
    Stat. Sin. (IF 0.968) Pub Date : 2018-10-23
    Tengfei Li,Fengchang Xie,Xiangnan Feng,Joseph G Ibrahim,Hongtu Zhu

    As an important part of modern health care, medical imaging data, which can be regarded as densely sampled functional data, have been widely used for diagnosis, screening, treatment, and prognosis, such as finding breast cancer through mammograms. The aim of this paper is to propose a functional linear regression model for using functional (or imaging) predictors to predict clinical outcomes (e.g.

    更新日期:2020-09-28
  • Asymptotic Behavior of Cox's Partial Likelihood and its Application to Variable Selection.
    Stat. Sin. (IF 0.968) Pub Date : 2018-10-9
    Runze Li,Jian-Jian Ren,Guangren Yang,Ye Yu

    For theoretical properties of variable selection procedures for Cox's model, we study the asymptotic behavior of partial likelihood for the Cox model. We find that the partial likelihood does not behave like an ordinary likelihood, whose sample average typically tends to its expected value, a finite number, in probability. Under some mild conditions, we prove that the sample average of partial likelihood

    更新日期:2020-09-28
  • Time-varying Hazards Model for Incorporating Irregularly Measured, High-Dimensional Biomarkers.
    Stat. Sin. (IF 0.968) Pub Date : 2020-9-22
    Xiang Li,Quefeng Li,Donglin Zeng,Karen Marder,Jane Paulsen,Yuanjia Wang

    Clinical studies with time-to-event outcomes often collect measurements of a large number of time-varying covariates over time (e.g., clinical assessments or neuroimaging biomarkers) to build time-sensitive prognostic model. An emerging challenge is that due to resource-intensive or invasive (e.g., lumbar puncture) data collection process, biomarkers may be measured infrequently and thus not available

    更新日期:2020-09-28
  • Evolutionary State-Space Model and Its Application to Time-Frequency Analysis of Local Field Potentials.
    Stat. Sin. (IF 0.968) Pub Date : 2020-8-11
    Xu Gao,Weining Shen,Babak Shahbaba,Norbert J Fortin,Hernando Ombao

    We propose an evolutionary state space model (E-SSM) for analyzing high dimensional brain signals whose statistical properties evolve over the course of a non-spatial memory experiment. Under E-SSM, brain signals are modeled as mixtures of components (e.g., AR(2) process) with oscillatory activity at pre-defined frequency bands. To account for the potential non-stationarity of these components (since

    更新日期:2020-09-28
  • The Lq- NORM LEARNING FOR ULTRAHIGH-DIMENSIONAL SURVIVAL DATA: AN INTEGRATIVE FRAMEWORK.
    Stat. Sin. (IF 0.968) Pub Date : 2020-8-4
    H G Hong,X Chen,J Kang,Y Li

    In the era of precision medicine, survival outcome data with high-throughput predictors are routinely collected. Models with an exceedingly large number of covariates are either infeasible to fit or likely to incur low predictability because of overfitting. Variable screening is key in identifying and removing irrelevant attributes. Recent years have seen a surge in screening methods, but most of them

    更新日期:2020-09-28
  • Generalized Regression Estimators with High-Dimensional Covariates.
    Stat. Sin. (IF 0.968) Pub Date : 2020-6-26
    Tram Ta,Jun Shao,Quefeng Li,Lei Wang

    Data from a large number of covariates with known population totals are frequently observed in survey studies. These auxiliary variables contain valuable information that can be incorporated into estimation of the population total of a survey variable to improve the estimation precision. We consider the generalized regression estimator formulated under the model-assisted framework in which a regression

    更新日期:2020-09-28
  • Discussion of Entropy Learning for Dynamic Treatment Regimes.
    Stat. Sin. (IF 0.968) Pub Date : 2019-11-05
    Min Qian,Bin Cheng

    更新日期:2019-11-01
  • Clustering in General Measurement Error Models.
    Stat. Sin. (IF 0.968) Pub Date : 2019-01-15
    Ya Su,Jill Reedy,Raymond J Carroll

    This paper is dedicated to the memory of Peter G. Hall. It concerns a deceptively simple question: if one observes variables corrupted with measurement error of possibly very complex form, can one recreate asymptotically the clusters that would have been found had there been no measurement error? We show that the answer is yes, and that the solution is surprisingly simple and general. The method itself

    更新日期:2019-11-01
  • Modeling subject-specific nonautonomous dynamics.
    Stat. Sin. (IF 0.968) Pub Date : 2018-02-10
    Siyuan Zhou,Debashis Paul,Jie Peng

    We consider modeling non-autonomous dynamical systems for a group of subjects. The proposed model involves a common baseline gradient function and a multiplicative time-dependent subject-specific effect that accounts for phase and amplitude variations in the rate of change across subjects. The baseline gradient function is represented in a spline basis and the subject-specific effect is modeled as

    更新日期:2019-11-01
  • TIME-VARYING COEFFICIENT MODELS FOR JOINT MODELING BINARY AND CONTINUOUS OUTCOMES IN LONGITUDINAL DATA.
    Stat. Sin. (IF 0.968) Pub Date : 2016-09-27
    Esra Kürüm,Runze Li,Saul Shiffman,Weixin Yao

    Motivated by an empirical analysis of ecological momentary assessment data (EMA) collected in a smoking cessation study, we propose a joint modeling technique for estimating the time-varying association between two intensively measured longitudinal responses: a continuous one and a binary one. A major challenge in joint modeling these responses is the lack of a multivariate distribution. We suggest

    更新日期:2019-11-01
  • Partial linear varying multi-index coefficient model for integrative gene-environment interactions.
    Stat. Sin. (IF 0.968) Pub Date : 2016-09-27
    Xu Liu,Yuehua Cui,Runze Li

    Gene-environment (G×E) interactions play key roles in many complex diseases. An increasing number of epidemiological studies have shown the combined effect of multiple environmental exposures on disease risk. However, no appropriate statistical models have been developed to conduct a rigorous assessment of such combined effects when G×E interactions are considered. In this paper, we propose a partial

    更新日期:2019-11-01
  • JOINT STRUCTURE SELECTION AND ESTIMATION IN THE TIME-VARYING COEFFICIENT COX MODEL.
    Stat. Sin. (IF 0.968) Pub Date : 2016-08-20
    Wei Xiao,Wenbin Lu,Hao Helen Zhang

    Time-varying coefficient Cox model has been widely studied and popularly used in survival data analysis due to its flexibility for modeling covariate effects. It is of great practical interest to accurately identify the structure of covariate effects in a time-varying coefficient Cox model, i.e. covariates with null effect, constant effect and truly time-varying effect, and estimate the corresponding

    更新日期:2019-11-01
  • Feature Screening in Ultrahigh Dimensional Cox's Model.
    Stat. Sin. (IF 0.968) Pub Date : 2016-07-16
    Guangren Yang,Ye Yu,Runze Li,Anne Buu

    Survival data with ultrahigh dimensional covariates such as genetic markers have been collected in medical studies and other fields. In this work, we propose a feature screening procedure for the Cox model with ultrahigh dimensional covariates. The proposed procedure is distinguished from the existing sure independence screening (SIS) procedures (Fan, Feng and Wu, 2010, Zhao and Li, 2012) in that the

    更新日期:2019-11-01
  • Prediction-based Termination Rule for Greedy Learning with Massive Data.
    Stat. Sin. (IF 0.968) Pub Date : 2016-05-10
    Chen Xu,Shaobo Lin,Jian Fang,Runze Li

    The appearance of massive data has become increasingly common in contemporary scientific research. When sample size n is huge, classical learning methods become computationally costly for the regression purpose. Recently, the orthogonal greedy algorithm (OGA) has been revitalized as an efficient alternative in the context of kernel-based statistical learning. In a learning problem, accurate and fast

    更新日期:2019-11-01
  • Marginal Structural Cox Models with Case-Cohort Sampling.
    Stat. Sin. (IF 0.968) Pub Date : 2016-04-09
    Hana Lee,Michael G Hudgens,Jianwen Cai,Stephen R Cole

    A common objective of biomedical cohort studies is assessing the effect of a time-varying treatment or exposure on a survival time. In the presence of time-varying confounders, marginal structural models fit using inverse probability weighting can be employed to obtain a consistent and asymptotically normal estimator of the causal effect of a time-varying treatment. This article considers estimation

    更新日期:2019-11-01
  • A Small-Sample Choice of the Tuning Parameter in Ridge Regression.
    Stat. Sin. (IF 0.968) Pub Date : 2016-03-18
    Philip S Boonstra,Bhramar Mukherjee,Jeremy M G Taylor

    We propose new approaches for choosing the shrinkage parameter in ridge regression, a penalized likelihood method for regularizing linear regression coefficients, when the number of observations is small relative to the number of parameters. Existing methods may lead to extreme choices of this parameter, which will either not shrink the coefficients enough or shrink them by too much. Within this "small-n

    更新日期:2019-11-01
  • Regularized Quantile Regression and Robust Feature Screening for Single Index Models.
    Stat. Sin. (IF 0.968) Pub Date : 2016-03-05
    Wei Zhong,Liping Zhu,Runze Li,Hengjian Cui

    We propose both a penalized quantile regression and an independence screening procedure to identify important covariates and to exclude unimportant ones for a general class of ultrahigh dimensional single-index models, in which the conditional distribution of the response depends on the covariates via a single-index structure. We observe that the linear quantile regression yields a consistent estimator

    更新日期:2019-11-01
  • Adaptive Estimation with Partially Overlapping Models.
    Stat. Sin. (IF 0.968) Pub Date : 2016-02-27
    Sunyoung Shin,Jason Fine,Yufeng Liu

    In many problems, one has several models of interest that capture key parameters describing the distribution of the data. Partially overlapping models are taken as models in which at least one covariate effect is common to the models. A priori knowledge of such structure enables efficient estimation of all model parameters. However, in practice, this structure may be unknown. We propose adaptive composite

    更新日期:2019-11-01
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