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Causal mediation analysis for stochastic interventions J. R. Stat. Soc. B (IF 3.278) Pub Date : 20200205
Iván Díaz; Nima S. HejaziMediation analysis in causal inference has traditionally focused on binary exposures and deterministic interventions, and a decomposition of the average treatment effect in terms of direct and indirect effects. We present an analogous decomposition of the population intervention effect, defined through stochastic interventions on the exposure. Population intervention effects provide a generalized framework

Functional models for time‐varying random objects J. R. Stat. Soc. B (IF 3.278) Pub Date : 20200224
Paromita Dubey; Hans‐Georg MüllerFunctional data analysis provides a popular toolbox of functional models for the analysis of samples of random functions that are real valued. In recent years, samples of time‐varying object data such as time‐varying networks that are not in a vector space have been increasingly collected. These data can be viewed as elements of a general metric space that lacks local or global linear structure and

Sparse principal component analysis via axis‐aligned random projections J. R. Stat. Soc. B (IF 3.278) Pub Date : 20200128
Milana Gataric; Tengyao Wang; Richard J. SamworthWe introduce a new method for sparse principal component analysis, based on the aggregation of eigenvector information from carefully selected axis‐aligned random projections of the sample covariance matrix. Unlike most alternative approaches, our algorithm is non‐iterative, so it is not vulnerable to a bad choice of initialization. We provide theoretical guarantees under which our principal subspace

Right singular vector projection graphs: fast high dimensional covariance matrix estimation under latent confounding J. R. Stat. Soc. B (IF 3.278) Pub Date : 20200131
Rajen D. Shah; Benjamin Frot; Gian‐Andrea Thanei; Nicolai MeinshausenWe consider the problem of estimating a high dimensional p×p covariance matrix Σ, given n observations of confounded data with covariance , where Γ is an unknown p×q matrix of latent factor loadings. We propose a simple and scalable estimator based on the projection onto the right singular vectors of the observed data matrix, which we call right singular vector projection (RSVP). Our theoretical analysis

Semisupervised inference for explained variance in high dimensional linear regression and its applications J. R. Stat. Soc. B (IF 3.278) Pub Date : 20200120
T. Tony Cai; Zijian GuoThe paper considers statistical inference for the explained variance under the high dimensional linear model Y=Xβ+ε in the semisupervised setting, where β is the regression vector and Σ is the design covariance matrix. A calibrated estimator, which efficiently integrates both labelled and unlabelled data, is proposed. It is shown that the estimator achieves the minimax optimal rate of convergence in

Model misspecification in approximate Bayesian computation: consequences and diagnostics J. R. Stat. Soc. B (IF 3.278) Pub Date : 20200108
David T. Frazier; Christian P. Robert; Judith RousseauWe analyse the behaviour of approximate Bayesian computation (ABC) when the model generating the simulated data differs from the actual data‐generating process, i.e. when the data simulator in ABC is misspecified. We demonstrate both theoretically and in simple, but practically relevant, examples that when the model is misspecified different versions of ABC can yield substantially different results

Doubly robust inference when combining probability and non‐probability samples with high dimensional data J. R. Stat. Soc. B (IF 3.278) Pub Date : 20200107
Shu Yang; Jae Kwang Kim; Rui SongWe consider integrating a non‐probability sample with a probability sample which provides high dimensional representative covariate information of the target population. We propose a two‐step approach for variable selection and finite population inference. In the first step, we use penalized estimating equations with folded concave penalties to select important variables and show selection consistency

Sumca: simple, unified, Monte‐Carlo‐assisted approach to second‐order unbiased mean‐squared prediction error estimation J. R. Stat. Soc. B (IF 3.278) Pub Date : 20200127
Jiming Jiang; Mahmoud TorabiWe propose a simple, unified, Monte‐Carlo‐assisted approach (called ‘Sumca’) to second‐order unbiased estimation of the mean‐squared prediction error (MSPE) of a small area predictor. The MSPE estimator proposed is easy to derive, has a simple expression and applies to a broad range of predictors that include the traditional empirical best linear unbiased predictor, empirical best predictor and post‐model‐selection

Exchangeable random measures for sparse and modular graphs with overlapping communities J. R. Stat. Soc. B (IF 3.278) Pub Date : 20200309
Adrien Todeschini; Xenia Miscouridou; François CaronWe propose a novel statistical model for sparse networks with overlapping community structure. The model is based on representing the graph as an exchangeable point process and naturally generalizes existing probabilistic models with overlapping block structure to the sparse regime. Our construction builds on vectors of completely random measures and has interpretable parameters, each node being assigned

Multiply robust causal inference with double‐negative control adjustment for categorical unmeasured confounding J. R. Stat. Soc. B (IF 3.278) Pub Date : 20200122
Xu Shi; Wang Miao; Jennifer C. Nelson; Eric J. Tchetgen TchetgenUnmeasured confounding is a threat to causal inference in observational studies. In recent years, the use of negative controls to mitigate unmeasured confounding has gained increasing recognition and popularity. Negative controls have a long‐standing tradition in laboratory sciences and epidemiology to rule out non‐causal explanations, although they have been used primarily for bias detection. Recently

A General Framework for Quantile Estimation with Incomplete Data. J. R. Stat. Soc. B (IF 3.278) Pub Date : 20191022
Peisong Han,Linglong Kong,Jiwei Zhao,Xingcai ZhouQuantile estimation has attracted significant research interests in recent years. However, there has been only a limited literature on quantile estimation in the presence of incomplete data. In this paper, we propose a general framework to address this problem. Our framework combines the two widely adopted approaches for missing data analysis, the imputation approach and the inverse probability weighting

Semiparametric Model for Bivariate Survival Data Subject to Biased Sampling. J. R. Stat. Soc. B (IF 3.278) Pub Date : 20190823
Jin Piao,Jing Ning,Yu ShenTo better understand the relationship between patient characteristics and their residual survival after an intermediate event such as the local cancer recurrence, it is of interest to identify patients with the intermediate event and then analyze their residual survival data. One challenge in analyzing such data is that the observed residual survival times tend to be longer than those in the target

Discussion on Covariateassisted ranking and screening for largescale twosample inference. J. R. Stat. Soc. B (IF 3.278) Pub Date : 20190611
Guo Yu,Jacob Bien,Daniela Witten 
An imputationregularized optimization algorithm for high dimensional missing data problems and beyond. J. R. Stat. Soc. B (IF 3.278) Pub Date : 20190528
Faming Liang,Bochao Jia,Jingnan Xue,Qizhai Li,Ye LuoMissing data are frequently encountered in high dimensional problems, but they are usually difficult to deal with by using standard algorithms, such as the expectationmaximization algorithm and its variants. To tackle this difficulty, some problemspecific algorithms have been developed in the literature, but there still lacks a general algorithm. This work is to fill the gap: we propose a general

False discovery rate control for high dimensional networks of quantile associations conditioning on covariates. J. R. Stat. Soc. B (IF 3.278) Pub Date : 20190507
Jichun Xie,Ruosha LiMotivated by gene coexpression pattern analysis, we propose a novel sample quantile contingency (SQUAC) statistic to infer quantile associations conditioning on covariates. It features enhanced flexibility in handling variables with both arbitrary distributions and complex association patterns conditioning on covariates. We first derive its asymptotic null distribution, and then develop a multipletesting

Spatially varying autoregressive models for prediction of new human immunodeficiency virus diagnoses. J. R. Stat. Soc. B (IF 3.278) Pub Date : 20190312
Lyndsay Shand,Bo Li,Trevor Park,Dolores AlbarracínIn demand of predicting new HIV diagnosis rates based on publicly available HIV data that is abundant in space but has few points in time, we propose a class of spatially varying autoregressive (SVAR) models compounded with conditional autoregressive (CAR) spatial correlation structures. We then propose to use the copula approach and a flexible CAR formulation to model the dependence between adjacent

Maximin Projection Learning for Optimal Treatment Decision with Heterogeneous Individualized Treatment Effects. J. R. Stat. Soc. B (IF 3.278) Pub Date : 20181218
Chengchun Shi,Rui Song,Wenbin Lu,Bo FuA saline feature of data from clinical trials and medical studies is inhomogeneity. Patients not only differ in baseline characteristics, but also the way they respond to treatment. Optimal individualized treatment regimes are developed to select effective treatments based on patient's heterogeneity. However, the optimal treatment regime might also vary for patients across different subgroups. In this

Multiple Matrix Gaussian Graphs Estimation. J. R. Stat. Soc. B (IF 3.278) Pub Date : 20181207
Yunzhang Zhu,Lexin LiMatrixvalued data, where the sampling unit is a matrix consisting of rows and columns of measurements, are emerging in numerous scientific and business applications. Matrix Gaussian graphical model is a useful tool to characterize the conditional dependence structure of rows and columns. In this article, we employ nonconvex penalization to tackle the estimation of multiple graphs from matrixvalued

An omnibus nonparametric test of equality in distribution for unknown functions. J. R. Stat. Soc. B (IF 3.278) Pub Date : 20181102
Alexander R Luedtke,Marco Carone,Mark J van der LaanWe present a novel family of nonparametric omnibus tests of the hypothesis that two unknown but estimable functions are equal in distribution when applied to the observed data structure. We developed these tests, which represent a generalization of the maximum mean discrepancy tests described in Gretton et al. [2006], using recent developments from the higherorder pathwise differentiability literature

Semiparametrically efficient estimation in quantile regression of secondary analysis. J. R. Stat. Soc. B (IF 3.278) Pub Date : 20181020
Liang Liang,Yanyuan Ma,Ying Wei,Raymond J CarrollAnalysing secondary outcomes is a common practice for casecontrol studies. Traditional secondary analysis employs either completely parametric models or conditional mean regression models to link the secondary outcome to covariates. In many situations, quantile regression models complement meanbased analyses and provide alternative new insights on the associations of interest. For example, biomedical

Bounded, efficient and multiply robust estimation of average treatment effects using instrumental variables. J. R. Stat. Soc. B (IF 3.278) Pub Date : 20180724
Linbo Wang,Eric Tchetgen TchetgenInstrumental variables (IVs) are widely used for estimating causal effects in the presence of unmeasured confounding. Under the standard IV model, however, the average treatment effect (ATE) is only partially identifiable. To address this, we propose novel assumptions that allow for identification of the ATE. Our identification assumptions are clearly separated from model assumptions needed for estimation

Testing for Marginal Linear Effects in Quantile Regression. J. R. Stat. Soc. B (IF 3.278) Pub Date : 20180327
Huixia Judy Wang,Ian W McKeague,Min QianThis paper develops a new marginal testing procedure to detect the presence of significant predictors associated with the conditional quantiles of a scalar response. The idea is to fit the marginal quantile regression on each predictor one at a time, and then base the test on the tstatistics associated with the most predictive predictors. A resampling method is devised to calibrate this test statistic

Robust estimation of encouragementdesign intervention effects transported across sites. J. R. Stat. Soc. B (IF 3.278) Pub Date : 20180130
Kara E Rudolph,Mark J van der LaanWe develop robust targeted maximum likelihood estimators (TMLE) for transporting intervention effects from one population to another. Specifically, we develop TMLE estimators for three transported estimands: intenttotreat average treatment effect (ATE) and complier ATE, which are relevant for encouragementdesign interventions and instrumental variable analyses, and the ATE of the exposure on the

ConcordanceAssisted Learning for Estimating Optimal Individualized Treatment Regimes. J. R. Stat. Soc. B (IF 3.278) Pub Date : 20180124
Caiyun Fan,Wenbin Lu,Rui Song,Yong ZhouIn this article, we propose a new concordanceassisted learning for estimating optimal individualized treatment regimes. We first introduce a type of concordance function for prescribing treatment and propose a robust rank regression method for estimating the concordance function. We then find treatment regimes, up to a threshold, to maximize the concordance function, named prescriptive index. Finally

Joint nonparametric correction estimator for excess relative risk regression in survival analysis with exposure measurement error. J. R. Stat. Soc. B (IF 3.278) Pub Date : 20180123
ChingYun Wang,Harry Cullings,Xiao Song,Kenneth J KopeckyObservational epidemiological studies often confront the problem of estimating exposuredisease relationships when the exposure is not measured exactly. In the paper, we investigate exposure measurement error in excess relative risk regression, which is a widely used model in radiation exposure effect research. In the study cohort, a surrogate variable is available for the true unobserved exposure

Optimal group testing designs for estimating prevalence with uncertain testing errors. J. R. Stat. Soc. B (IF 3.278) Pub Date : 20171219
ShihHao Huang,MongNa Lo Huang,Kerby Shedden,Weng Kee WongWe construct optimal designs for group testing experiments where the goal is to estimate the prevalence of a trait using a test with uncertain sensitivity and specificity. Using optimal design theory for approximate designs, we show that the most efficient design for simultaneously estimating the prevalence, sensitivity, and specificity requires three different group sizes with equal frequencies. However

Sparse graphs using exchangeable random measures. J. R. Stat. Soc. B (IF 3.278) Pub Date : 20171205
François Caron,Emily B FoxStatistical network modelling has focused on representing the graph as a discrete structure, namely the adjacency matrix. When assuming exchangeability of this arraywhich can aid in modelling, computations and theoretical analysisthe AldousHoover theorem informs us that the graph is necessarily either dense or empty. We instead consider representing the graph as an exchangeable random measure and

EigenPrism: inference for high dimensional signaltonoise ratios. J. R. Stat. Soc. B (IF 3.278) Pub Date : 20171107
Lucas Janson,Rina Foygel Barber,Emmanuel CandèsConsider the following three important problems in statistical inference, namely, constructing confidence intervals for (1) the error of a highdimensional (p > n) regression estimator, (2) the linear regression noise level, and (3) the genetic signaltonoise ratio of a continuousvalued trait (related to the heritability). All three problems turn out to be closely related to the littlestudied problem

Estimation of the false discovery proportion with unknown dependence. J. R. Stat. Soc. B (IF 3.278) Pub Date : 20171024
Jianqing Fan,Xu HanLargescale multiple testing with correlated test statistics arises frequently in many scientific research. Incorporating correlation information in approximating false discovery proportion has attracted increasing attention in recent years. When the covariance matrix of test statistics is known, Fan, Han & Gu (2012) provided an accurate approximation of False Discovery Proportion (FDP) under arbitrary

Nonparametric methods for doubly robust estimation of continuous treatment effects. J. R. Stat. Soc. B (IF 3.278) Pub Date : 20171011
Edward H Kennedy,Zongming Ma,Matthew D McHugh,Dylan S SmallContinuous treatments (e.g., doses) arise often in practice, but many available causal effect estimators are limited by either requiring parametric models for the effect curve, or by not allowing doubly robust covariate adjustment. We develop a novel kernel smoothing approach that requires only mild smoothness assumptions on the effect curve, and still allows for misspecification of either the treatment

On Estimation of Optimal Treatment Regimes For Maximizing tYear Survival Probability. J. R. Stat. Soc. B (IF 3.278) Pub Date : 20171007
Runchao Jiang,Wenbin Lu,Rui Song,Marie DavidianA treatment regime is a deterministic function that dictates personalized treatment based on patients' individual prognostic information. There is increasing interest in finding optimal treatment regimes, which determine treatment at one or more treatment decision points so as to maximize expected longterm clinical outcome, where larger outcomes are preferred. For chronic diseases such as cancer or

Change point estimation in high dimensional Markov randomfield models. J. R. Stat. Soc. B (IF 3.278) Pub Date : 20170830
Sandipan Roy,Yves Atchadé,George MichailidisThis paper investigates a changepoint estimation problem in the context of highdimensional Markov random field models. Changepoints represent a key feature in many dynamically evolving network structures. The changepoint estimate is obtained by maximizing a profile penalized pseudolikelihood function under a sparsity assumption. We also derive a tight bound for the estimate, up to a logarithmic

Mediation analysis with time varying exposures and mediators. J. R. Stat. Soc. B (IF 3.278) Pub Date : 20170822
Tyler J VanderWeele,Eric J Tchetgen TchetgenIn this paper we consider causal mediation analysis when exposures and mediators vary over time. We give nonparametric identification results, discuss parametric implementation, and also provide a weighting approach to direct and indirect effects based on combining the results of two marginal structural models. We also discuss how our results give rise to a causal interpretation of the effect estimates

Estimation of high dimensional mean regression in the absence of symmetry and light tail assumptions. J. R. Stat. Soc. B (IF 3.278) Pub Date : 20170510
Jianqing Fan,Quefeng Li,Yuyan WangData subject to heavytailed errors are commonly encountered in various scientific fields. To address this problem, procedures based on quantile regression and Least Absolute Deviation (LAD) regression have been developed in recent years. These methods essentially estimate the conditional median (or quantile) function. They can be very different from the conditional mean functions, especially when

Causal analysis of ordinal treatments and binary outcomes under truncation by death. J. R. Stat. Soc. B (IF 3.278) Pub Date : 20170502
Linbo Wang,Thomas S Richardson,XiaoHua ZhouIt is common that in multiarm randomized trials, the outcome of interest is "truncated by death," meaning that it is only observed or welldefined conditioning on an intermediate outcome. In this case, in addition to pairwise contrasts, the joint inference for all treatment arms is also of interest. Under a monotonicity assumption we present methods for both pairwise and joint causal analyses of ordinal

Efficient Estimation of Semiparametric Transformation Models for the Cumulative Incidence of Competing Risks. J. R. Stat. Soc. B (IF 3.278) Pub Date : 20170228
Lu Mao,D Y LinThe cumulative incidence is the probability of failure from the cause of interest over a certain time period in the presence of other risks. A semiparametric regression model proposed by Fine and Gray (1999) has become the method of choice for formulating the effects of covariates on the cumulative incidence. Its estimation, however, requires modeling of the censoring distribution and is not statistically

A general framework for updating belief distributions. J. R. Stat. Soc. B (IF 3.278) Pub Date : 20161115
P G Bissiri,C C Holmes,S G WalkerWe propose a framework for general Bayesian inference. We argue that a valid update of a prior belief distribution to a posterior can be made for parameters which are connected to observations through a loss function rather than the traditional likelihood function, which is recovered as a special case. Modern application areas make it increasingly challenging for Bayesians to attempt to model the true

The lasso for high dimensional regression with a possible change point. J. R. Stat. Soc. B (IF 3.278) Pub Date : 20160923
Sokbae Lee,Myung Hwan Seo,Youngki ShinWe consider a high dimensional regression model with a possible change point due to a covariate threshold and develop the lasso estimator of regression coefficients as well as the threshold parameter. Our lasso estimator not only selects covariates but also selects a model between linear and threshold regression models. Under a sparsity assumption, we derive nonasymptotic oracle inequalities for both

Making the cut: improved ranking and selection for largescale inference. J. R. Stat. Soc. B (IF 3.278) Pub Date : 20160830
Nicholas C Henderson,Michael A NewtonIdentifying leading measurement units from a large collection is a common inference task in various domains of largescale inference. Testing approaches, which measure evidence against a null hypothesis rather than effect magnitude, tend to overpopulate lists of leading units with those associated with low measurement error. By contrast, local maximum likelihood (ML) approaches tend to favor units

Globally efficient nonparametric inference of average treatment effects by empirical balancing calibration weighting. J. R. Stat. Soc. B (IF 3.278) Pub Date : 20160628
Kwun Chuen Gary Chan,Sheung Chi Phillip Yam,Zheng ZhangThe estimation of average treatment effects based on observational data is extremely important in practice and has been studied by generations of statisticians under different frameworks. Existing globally efficient estimators require nonparametric estimation of a propensity score function, an outcome regression function or both, but their performance can be poor in practical sample sizes. Without

Regression Models on Riemannian Symmetric Spaces. J. R. Stat. Soc. B (IF 3.278) Pub Date : 20160320
Emil Cornea,Hongtu Zhu,Peter Kim,Joseph G IbrahimThe aim of this paper is to develop a general regression framework for the analysis of manifoldvalued response in a Riemannian symmetric space (RSS) and its association with multiple covariates of interest, such as age or gender, in Euclidean space. Such RSSvalued data arises frequently in medical imaging, surface modeling, and computer vision, among many others. We develop an intrinsic regression

Joint Estimation of Multiple Graphical Models from High Dimensional Time Series. J. R. Stat. Soc. B (IF 3.278) Pub Date : 20160301
Huitong Qiu,Fang Han,Han Liu,Brian CaffoIn this manuscript we consider the problem of jointly estimating multiple graphical models in high dimensions. We assume that the data are collected from n subjects, each of which consists of T possibly dependent observations. The graphical models of subjects vary, but are assumed to change smoothly corresponding to a measure of closeness between subjects. We propose a kernel based method for jointly

Semiparametric Estimation in the Secondary Analysis of CaseControl Studies. J. R. Stat. Soc. B (IF 3.278) Pub Date : 20160203
Yanyuan Ma,Raymond J CarrollWe study the regression relationship among covariates in casecontrol data, an area known as the secondary analysis of casecontrol studies. The context is such that only the form of the regression mean is specified, so that we allow an arbitrary regression error distribution, which can depend on the covariates and thus can be heteroscedastic. Under mild regularity conditions we establish the theoretical

Variable Selection for Support Vector Machines in Moderately High Dimensions. J. R. Stat. Soc. B (IF 3.278) Pub Date : 20160119
Xiang Zhang,Yichao Wu,Lan Wang,Runze LiThe support vector machine (SVM) is a powerful binary classification tool with high accuracy and great flexibility. It has achieved great success, but its performance can be seriously impaired if many redundant covariates are included. Some efforts have been devoted to studying variable selection for SVMs, but asymptotic properties, such as variable selection consistency, are largely unknown when the

Regression analysis of sparse asynchronous longitudinal data. J. R. Stat. Soc. B (IF 3.278) Pub Date : 20151117
Hongyuan Cao,Donglin Zeng,Jason P FineWe consider estimation of regression models for sparse asynchronous longitudinal observations, where timedependent responses and covariates are observed intermittently within subjects. Unlike with synchronous data, where the response and covariates are observed at the same time point, with asynchronous data, the observation times are mismatched. Simple kernelweighted estimating equations are proposed

Sparsifying the Fisher Linear Discriminant by Rotation. J. R. Stat. Soc. B (IF 3.278) Pub Date : 20151030
Ning Hao,Bin Dong,Jianqing FanMany high dimensional classification techniques have been proposed in the literature based on sparse linear discriminant analysis (LDA). To efficiently use them, sparsity of linear classifiers is a prerequisite. However, this might not be readily available in many applications, and rotations of data are required to create the needed sparsity. In this paper, we propose a family of rotations to create

Frequentist accuracy of Bayesian estimates. J. R. Stat. Soc. B (IF 3.278) Pub Date : 20150620
Bradley EfronIn the absence of relevant prior experience, popular Bayesian estimation techniques usually begin with some form of "uninformative" prior distribution intended to have minimal inferential influence. Bayes rule will still produce nicelooking estimates and credible intervals, but these lack the logical force attached to experiencebased priors and require further justification. This paper concerns the

Quasilikelihood for Spatial Point Processes. J. R. Stat. Soc. B (IF 3.278) Pub Date : 20150605
Yongtao Guan,Abdollah Jalilian,Rasmus WaagepetersenFitting regression models for intensity functions of spatial point processes is of great interest in ecological and epidemiological studies of association between spatially referenced events and geographical or environmental covariates. When Cox or cluster process models are used to accommodate clustering not accounted for by the available covariates, likelihood based inference becomes computationally

Semiparametric transformation models for causal inference in time to event studies with allornothing compliance. J. R. Stat. Soc. B (IF 3.278) Pub Date : 20150415
Wen Yu,Kani Chen,Michael E Sobel,Zhiliang YingWe consider causal inference in randomized survival studies with right censored outcomes and allornothing compliance, using semiparametric transformation models to estimate the distribution of survival times in treatment and control groups, conditional on covariates and latent compliance type. Estimands depending on these distributions, for example, the complier average causal effect (CACE), the

Joint modelling of repeated measurements and timetoevent outcomes: flexible model specification and exact likelihood inference. J. R. Stat. Soc. B (IF 3.278) Pub Date : 20150414
Jessica Barrett,Peter Diggle,Robin Henderson,David TaylorRobinsonRandom effects or shared parameter models are commonly advocated for the analysis of combined repeated measurement and event history data, including dropout from longitudinal trials. Their use in practical applications has generally been limited by computational cost and complexity, meaning that only simple special cases can be fitted by using readily available software. We propose a new approach that

Doubly robust estimation of the local average treatment effect curve. J. R. Stat. Soc. B (IF 3.278) Pub Date : 20150211
Elizabeth L Ogburn,Andrea Rotnitzky,James M RobinsWe consider estimation of the causal effect of a binary treatment on an outcome, conditionally on covariates, from observational studies or natural experiments in which there is a binary instrument for treatment. We describe a doubly robust, locally efficient estimator of the parameters indexing a model for the local average treatment effect conditionally on covariates V when randomization of the instrument

Marginally specified priors for nonparametric Bayesian estimation. J. R. Stat. Soc. B (IF 3.278) Pub Date : 20150211
David C Kessler,Peter D Hoff,David B DunsonPrior specification for nonparametric Bayesian inference involves the difficult task of quantifying prior knowledge about a parameter of high, often infinite, dimension. A statistician is unlikely to have informed opinions about all aspects of such a parameter but will have real information about functionals of the parameter, such as the population mean or variance. The paper proposes a new framework

False Discovery Control in LargeScale Spatial Multiple Testing. J. R. Stat. Soc. B (IF 3.278) Pub Date : 20150203
Wenguang Sun,Brian J Reich,T Tony Cai,Michele Guindani,Armin SchwartzmanThis article develops a unified theoretical and computational framework for false discovery control in multiple testing of spatial signals. We consider both pointwise and clusterwise spatial analyses, and derive oracle procedures which optimally control the false discovery rate, false discovery exceedance and false cluster rate, respectively. A datadriven finite approximation strategy is developed

Variance Function Partially Linear SingleIndex Models1. J. R. Stat. Soc. B (IF 3.278) Pub Date : 20150203
Heng Lian,Hua Liang,Raymond J CarrollWe consider heteroscedastic regression models where the mean function is a partially linear single index model and the variance function depends upon a generalized partially linear single index model. We do not insist that the variance function depend only upon the mean function, as happens in the classical generalized partially linear single index model. We develop efficient and practical estimation

Quantile Regression Adjusting for Dependent Censoring from SemiCompeting Risks. J. R. Stat. Soc. B (IF 3.278) Pub Date : 20150113
Ruosha Li,Limin PengIn this work, we study quantile regression when the response is an event time subject to potentially dependent censoring. We consider the semicompeting risks setting, where time to censoring remains observable after the occurrence of the event of interest. While such a scenario frequently arises in biomedical studies, most of current quantile regression methods for censored data are not applicable

Estimates and Standard Errors for Ratios of Normalizing Constants from Multiple Markov Chains via Regeneration. J. R. Stat. Soc. B (IF 3.278) Pub Date : 20140901
Hani Doss,Aixin TanIn the classical biased sampling problem, we have k densities π1(·), …, πk (·), each known up to a normalizing constant, i.e. for l = 1, …, k, πl (·) = νl (·)/ml , where νl (·) is a known function and ml is an unknown constant. For each l, we have an iid sample from πl ,·and the problem is to estimate the ratios ml/ms for all l and all s. This problem arises frequently in several situations in both

Structured functional additive regression in reproducing kernel Hilbert spaces. J. R. Stat. Soc. B (IF 3.278) Pub Date : 20140712
Hongxiao Zhu,Fang Yao,Hao Helen ZhangFunctional additive models (FAMs) provide a flexible yet simple framework for regressions involving functional predictors. The utilization of datadriven basis in an additive rather than linear structure naturally extends the classical functional linear model. However, the critical issue of selecting nonlinear additive components has been less studied. In this work, we propose a new regularization

The joint graphical lasso for inverse covariance estimation across multiple classes. J. R. Stat. Soc. B (IF 3.278) Pub Date : 20140513
Patrick Danaher,Pei Wang,Daniela M WittenWe consider the problem of estimating multiple related Gaussian graphical models from a highdimensional data set with observations belonging to distinct classes. We propose the joint graphical lasso, which borrows strength across the classes in order to estimate multiple graphical models that share certain characteristics, such as the locations or weights of nonzero edges. Our approach is based upon

Semiparametric Inference for Data with a Continuous Outcome from a TwoPhase Probability Dependent Sampling Scheme. J. R. Stat. Soc. B (IF 3.278) Pub Date : 20140417
Haibo Zhou,Wangli Xu,Donglin Zeng,Jianwen CaiMultiphased designs and biased sampling designs are two of the well recognized approaches to enhance study efficiency. In this paper, we propose a new and costeffective sampling design, the twophase probability dependent sampling design (PDS), for studies with a continuous outcome. This design will enable investigators to make efficient use of resources by targeting more informative subjects for

Regularized matrix regression. J. R. Stat. Soc. B (IF 3.278) Pub Date : 20140322
Hua Zhou,Lexin LiModern technologies are producing a wealth of data with complex structures. For instance, in twodimensional digital imaging, flow cytometry and electroencephalography, matrixtype covariates frequently arise when measurements are obtained for each combination of two underlying variables. To address scientific questions arising from those data, new regression methods that take matrices as covariates