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Graphical models for extremes J. R. Stat. Soc. B (IF 3.965) Pub Date : 20200629
Sebastian Engelke; Adrien S. HitzConditional independence, graphical models and sparsity are key notions for parsimonious statistical models and for understanding the structural relationships in the data. The theory of multivariate and spatial extremes describes the risk of rare events through asymptotically justified limit models such as max‐stable and multivariate Pareto distributions. Statistical modelling in this field has been

Quasi‐Bayes properties of a procedure for sequential learning in mixture models J. R. Stat. Soc. B (IF 3.965) Pub Date : 20200629
Sandra Fortini; Sonia PetroneBayesian methods are often optimal, yet increasing pressure for fast computations, especially with streaming data, brings renewed interest in faster, possibly suboptimal, solutions. The extent to which these algorithms approximate Bayesian solutions is a question of interest, but often unanswered. We propose a methodology to address this question in predictive settings, when the algorithm can be reinterpreted

A scalable estimate of the out‐of‐sample prediction error via approximate leave‐one‐out cross‐validation J. R. Stat. Soc. B (IF 3.965) Pub Date : 20200620
Kamiar Rahnama Rad; Arian MalekiThe paper considers the problem of out‐of‐sample risk estimation under the high dimensional settings where standard techniques such as K ‐fold cross‐validation suffer from large biases. Motivated by the low bias of the leave‐one‐out cross‐validation method, we propose a computationally efficient closed form approximate leave‐one‐out formula ALO for a large class of regularized estimators. Given the

Adaptive designs for optimal observed Fisher information J. R. Stat. Soc. B (IF 3.965) Pub Date : 20200615
Adam LaneExpected Fisher information can be found a priori and as a result its inverse is the primary variance approximation used in the design of experiments. This is in contrast with the common claim that the inverse of the observed Fisher information is a better approximation of the variance of the maximum likelihood estimator. Observed Fisher information cannot be known a priori ; however, if an experiment

Optimal alpha spending for sequential analysis with binomial data J. R. Stat. Soc. B (IF 3.965) Pub Date : 20200615
Ivair R. Silva; Martin Kulldorff; W. Katherine YihFor sequential analysis hypothesis testing, various alpha spending functions have been proposed. Given a prespecified overall alpha level and power, we derive the optimal alpha spending function that minimizes the expected time to signal for continuous as well as group sequential analysis. If there is also a restriction on the maximum sample size or on the expected sample size, we do the same. Alternatively

A unified data‐adaptive framework for high dimensional change point detection J. R. Stat. Soc. B (IF 3.965) Pub Date : 20200612
Bin Liu; Cheng Zhou; Xinsheng Zhang; Yufeng LiuIn recent years, change point detection for a high dimensional data sequence has become increasingly important in many scientific fields such as biology and finance. The existing literature develops a variety of methods designed for either a specified parameter (e.g. the mean or covariance) or a particular alternative pattern (sparse or dense), but not for both scenarios simultaneously. To overcome

Visualizing the effects of predictor variables in black box supervised learning models J. R. Stat. Soc. B (IF 3.965) Pub Date : 20200611
Daniel W. Apley; Jingyu ZhuIn many supervised learning applications, understanding and visualizing the effects of the predictor variables on the predicted response is of paramount importance. A shortcoming of black box supervised learning models (e.g. complex trees, neural networks, boosted trees, random forests, nearest neighbours, local kernel‐weighted methods and support vector regression) in this regard is their lack of

A flexible framework for hypothesis testing in high dimensions J. R. Stat. Soc. B (IF 3.965) Pub Date : 20200521
Adel Javanmard; Jason D. LeeHypothesis testing in the linear regression model is a fundamental statistical problem. We consider linear regression in the high dimensional regime where the number of parameters exceeds the number of samples (p >n ). To make informative inference, we assume that the model is approximately sparse, i.e. the effect of covariates on the response can be well approximated by conditioning on a relatively

Goodness‐of‐fit testing in high dimensional generalized linear models J. R. Stat. Soc. B (IF 3.965) Pub Date : 20200515
Jana Janková; Rajen D. Shah; Peter Bühlmann; Richard J. SamworthWe propose a family of tests to assess the goodness of fit of a high dimensional generalized linear model. Our framework is flexible and may be used to construct an omnibus test or directed against testing specific non‐linearities and interaction effects, or for testing the significance of groups of variables. The methodology is based on extracting left‐over signal in the residuals from an initial

Causal isotonic regression J. R. Stat. Soc. B (IF 3.965) Pub Date : 20200513
Ted Westling; Peter Gilbert; Marco CaroneIn observational studies, potential confounders may distort the causal relationship between an exposure and an outcome. However, under some conditions, a causal dose–response curve can be recovered by using the G ‐computation formula. Most classical methods for estimating such curves when the exposure is continuous rely on restrictive parametric assumptions, which carry significant risk of model misspecification

Robust testing in generalized linear models by sign flipping score contributions J. R. Stat. Soc. B (IF 3.965) Pub Date : 20200511
Jesse Hemerik; Jelle J. Goeman; Livio FinosGeneralized linear models are often misspecified because of overdispersion, heteroscedasticity and ignored nuisance variables. Existing quasi‐likelihood methods for testing in misspecified models often do not provide satisfactory type I error rate control. We provide a novel semiparametric test, based on sign flipping individual score contributions. The parameter tested is allowed to be multi‐dimensional

Testing relevant hypotheses in functional time series via self‐normalization J. R. Stat. Soc. B (IF 3.965) Pub Date : 20200509
Holger Dette; Kevin Kokot; Stanislav VolgushevWe develop methodology for testing relevant hypotheses about functional time series in a tuning‐free way. Instead of testing for exact equality, e.g. for the equality of two mean functions from two independent time series, we propose to test the null hypothesis of no relevant deviation. In the two‐sample problem this means that an L 2 ‐distance between the two mean functions is smaller than a prespecified

Inference for two‐stage sampling designs J. R. Stat. Soc. B (IF 3.965) Pub Date : 20200506
Guillaume Chauvet; Audrey‐Anne ValléeTwo‐stage sampling designs are commonly used for household and health surveys. To produce reliable estimators with associated confidence intervals, some basic statistical properties like consistency and asymptotic normality of the Horvitz–Thompson estimator are desirable, along with the consistency of associated variance estimators. These properties have been mainly studied for single‐stage sampling

Unbiased Markov chain Monte Carlo methods with couplings J. R. Stat. Soc. B (IF 3.965) Pub Date : 20200506
Pierre E. Jacob; John O’Leary; Yves F. AtchadéMarkov chain Monte Carlo (MCMC) methods provide consistent approximations of integrals as the number of iterations goes to ∞. MCMC estimators are generally biased after any fixed number of iterations. We propose to remove this bias by using couplings of Markov chains together with a telescopic sum argument of Glynn and Rhee. The resulting unbiased estimators can be computed independently in parallel

On bandwidth choice for spatial data density estimation J. R. Stat. Soc. B (IF 3.965) Pub Date : 20200421
Zhenyu Jiang; Nengxiang Ling; Zudi Lu; Dag Tj⊘stheim; Qiang ZhangBandwidth choice is crucial in spatial kernel estimation in exploring non‐Gaussian complex spatial data. The paper investigates the choice of adaptive and non‐adaptive bandwidths for density estimation given data on a spatial lattice. An adaptive bandwidth depends on local data and hence adaptively conforms with local features of the spatial data. We propose a spatial cross‐validation (SCV) choice

Optimal, two‐stage, adaptive enrichment designs for randomized trials, using sparse linear programming J. R. Stat. Soc. B (IF 3.965) Pub Date : 20200417
Michael Rosenblum; Ethan X. Fang; Han LiuAdaptive enrichment designs involve preplanned rules for modifying enrolment criteria based on accruing data in a randomized trial. We focus on designs where the overall population is partitioned into two predefined subpopulations, e.g. based on a biomarker or risk score measured at baseline. The goal is to learn which populations benefit from an experimental treatment. Two critical components of adaptive

Robust estimation via robust gradient estimation J. R. Stat. Soc. B (IF 3.965) Pub Date : 20200415
Adarsh Prasad; Arun Sai Suggala; Sivaraman Balakrishnan; Pradeep RavikumarWe provide a new computationally efficient class of estimators for risk minimization. We show that these estimators are robust for general statistical models, under varied robustness settings, including in the classical Huber ε ‐contamination model, and in heavy‐tailed settings. Our workhorse is a novel robust variant of gradient descent, and we provide conditions under which our gradient descent variant

Exchangeable random measures for sparse and modular graphs with overlapping communities J. R. Stat. Soc. B (IF 3.965) 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

Functional models for time‐varying random objects J. R. Stat. Soc. B (IF 3.965) 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

Causal mediation analysis for stochastic interventions J. R. Stat. Soc. B (IF 3.965) 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

Right singular vector projection graphs: fast high dimensional covariance matrix estimation under latent confounding J. R. Stat. Soc. B (IF 3.965) 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

Sparse principal component analysis via axis‐aligned random projections J. R. Stat. Soc. B (IF 3.965) 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

Sumca: simple, unified, Monte‐Carlo‐assisted approach to second‐order unbiased mean‐squared prediction error estimation J. R. Stat. Soc. B (IF 3.965) 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

Multiply robust causal inference with double‐negative control adjustment for categorical unmeasured confounding J. R. Stat. Soc. B (IF 3.965) 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

Semisupervised inference for explained variance in high dimensional linear regression and its applications J. R. Stat. Soc. B (IF 3.965) 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 3.965) 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 3.965) 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

A General Framework for Quantile Estimation with Incomplete Data. J. R. Stat. Soc. B (IF 3.965) 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 3.965) 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 3.965) 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 3.965) 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 3.965) 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

Maximin Projection Learning for Optimal Treatment Decision with Heterogeneous Individualized Treatment Effects. J. R. Stat. Soc. B (IF 3.965) 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 3.965) 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 3.965) 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 3.965) 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 3.965) 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 3.965) 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

Spatially varying autoregressive models for prediction of new human immunodeficiency virus diagnoses. J. R. Stat. Soc. B (IF 3.965) Pub Date : 20180312
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

Robust estimation of encouragementdesign intervention effects transported across sites. J. R. Stat. Soc. B (IF 3.965) 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 3.965) 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 3.965) 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 3.965) 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 3.965) 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 3.965) 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 3.965) 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 3.965) 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 3.965) 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 3.965) 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 3.965) 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 3.965) 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 3.965) 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 3.965) 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 3.965) 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 3.965) 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 3.965) 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 3.965) 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 3.965) 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 3.965) 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 3.965) 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