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Journal of the Royal Statistical Society. Series B, Statistical Methodology

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https://www.readbyqxmd.com/read/27840585/a-general-framework-for-updating-belief-distributions
#1
P G Bissiri, C C Holmes, S G Walker
We 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 data-generating mechanism. For instance, when the object of interest is low dimensional, such as a mean or median, it is cumbersome to have to achieve this via a complete model for the whole data distribution...
November 2016: Journal of the Royal Statistical Society. Series B, Statistical Methodology
https://www.readbyqxmd.com/read/27570475/making-the-cut-improved-ranking-and-selection-for-large-scale-inference
#2
Nicholas C Henderson, Michael A Newton
Identifying leading measurement units from a large collection is a common inference task in various domains of large-scale 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 with high measurement error. Available Bayesian and empirical Bayesian approaches rely on specialized loss functions that result in similar deficiencies...
September 2016: Journal of the Royal Statistical Society. Series B, Statistical Methodology
https://www.readbyqxmd.com/read/27346982/globally-efficient-non-parametric-inference-of-average-treatment-effects-by-empirical-balancing-calibration-weighting
#3
Kwun Chuen Gary Chan, Sheung Chi Phillip Yam, Zheng Zhang
The 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 non-parametric estimation of a propensity score function, an outcome regression function or both, but their performance can be poor in practical sample sizes. Without explicitly estimating either functions, we consider a wide class calibration weights constructed to attain an exact three-way balance of the moments of observed covariates among the treated, the control, and the combined group...
June 2016: Journal of the Royal Statistical Society. Series B, Statistical Methodology
https://www.readbyqxmd.com/read/26924939/joint-estimation-of-multiple-graphical-models-from-high-dimensional-time-series
#4
Huitong Qiu, Fang Han, Han Liu, Brian Caffo
In 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 estimating all graphical models. Theoretically, under a double asymptotic framework, where both (T, n) and the dimension d can increase, we provide the explicit rate of convergence in parameter estimation...
March 1, 2016: Journal of the Royal Statistical Society. Series B, Statistical Methodology
https://www.readbyqxmd.com/read/27656104/the-lasso-for-high-dimensional-regression-with-a-possible-change-point
#5
Sokbae Lee, Myung Hwan Seo, Youngki Shin
We 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 non-asymptotic oracle inequalities for both the prediction risk and the l1-estimation loss for regression coefficients. Since the lasso estimator selects variables simultaneously, we show that oracle inequalities can be established without pretesting the existence of the threshold effect...
January 2016: Journal of the Royal Statistical Society. Series B, Statistical Methodology
https://www.readbyqxmd.com/read/26834506/semiparametric-estimation-in-the-secondary-analysis-of-case-control-studies
#6
Yanyuan Ma, Raymond J Carroll
We study the regression relationship among covariates in case-control data, an area known as the secondary analysis of case-control 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 identifiability of such models. Previous work in this context has either (a) specified a fully parametric distribution for the regression errors, (b) specified a homoscedastic distribution for the regression errors, (c) has specified the rate of disease in the population (we refer this as true population), or (d) has made a rare disease approximation...
January 2016: Journal of the Royal Statistical Society. Series B, Statistical Methodology
https://www.readbyqxmd.com/read/26778916/variable-selection-for-support-vector-machines-in-moderately-high-dimensions
#7
Xiang Zhang, Yichao Wu, Lan Wang, Runze Li
The 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 number of predictors diverges to infinity. In this work, we establish a unified theory for a general class of nonconvex penalized SVMs...
January 2016: Journal of the Royal Statistical Society. Series B, Statistical Methodology
https://www.readbyqxmd.com/read/26568699/regression-analysis-of-sparse-asynchronous-longitudinal-data
#8
Hongyuan Cao, Donglin Zeng, Jason P Fine
We consider estimation of regression models for sparse asynchronous longitudinal observations, where time-dependent 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 kernel-weighted estimating equations are proposed for generalized linear models with either time invariant or time-dependent coefficients under smoothness assumptions for the covariate processes which are similar to those for synchronous data...
September 2015: Journal of the Royal Statistical Society. Series B, Statistical Methodology
https://www.readbyqxmd.com/read/26512210/sparsifying-the-fisher-linear-discriminant-by-rotation
#9
Ning Hao, Bin Dong, Jianqing Fan
Many 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 the required sparsity. The basic idea is to use the principal components of the sample covariance matrix of the pooled samples and its variants to rotate the data first and to then apply an existing high dimensional classifier...
September 1, 2015: Journal of the Royal Statistical Society. Series B, Statistical Methodology
https://www.readbyqxmd.com/read/26089740/frequentist-accuracy-of-bayesian-estimates
#10
Bradley Efron
In 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 nice-looking estimates and credible intervals, but these lack the logical force attached to experience-based priors and require further justification. This paper concerns the frequentist assessment of Bayes estimates. A simple formula is shown to give the frequentist standard deviation of a Bayesian point estimate...
June 2015: Journal of the Royal Statistical Society. Series B, Statistical Methodology
https://www.readbyqxmd.com/read/26041970/quasi-likelihood-for-spatial-point-processes
#11
Yongtao Guan, Abdollah Jalilian, Rasmus Waagepetersen
Fitting 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 cumbersome due to the complicated nature of the likelihood function and the associated score function. It is therefore of interest to consider alternative more easily computable estimating functions...
June 1, 2015: Journal of the Royal Statistical Society. Series B, Statistical Methodology
https://www.readbyqxmd.com/read/25870521/semiparametric-transformation-models-for-causal-inference-in-time-to-event-studies-with-all-or-nothing-compliance
#12
Wen Yu, Kani Chen, Michael E Sobel, Zhiliang Ying
We consider causal inference in randomized survival studies with right censored outcomes and all-or-nothing 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 complier effect on survival beyond time t, and the complier quantile effect are then considered. Maximum likelihood is used to estimate the parameters of the transformation models, using a specially designed expectation-maximization (EM) algorithm to overcome the computational difficulties created by the mixture structure of the problem and the infinite dimensional parameter in the transformation models...
March 1, 2015: Journal of the Royal Statistical Society. Series B, Statistical Methodology
https://www.readbyqxmd.com/read/25663814/doubly-robust-estimation-of-the-local-average-treatment-effect-curve
#13
Elizabeth L Ogburn, Andrea Rotnitzky, James M Robins
We 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 is only true conditionally on a high dimensional vector of covariates X, possibly bigger than V. We discuss the surprising result that inference is identical to inference for the parameters of a model for an additive treatment effect on the treated conditionally on V that assumes no treatment-instrument interaction...
March 2015: Journal of the Royal Statistical Society. Series B, Statistical Methodology
https://www.readbyqxmd.com/read/25866468/joint-modelling-of-repeated-measurements-and-time-to-event-outcomes-flexible-model-specification-and-exact-likelihood-inference
#14
Jessica Barrett, Peter Diggle, Robin Henderson, David Taylor-Robinson
Random 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 exploits recent distributional results for the extended skew normal family to allow exact likelihood inference for a flexible class of random-effects models...
January 2015: Journal of the Royal Statistical Society. Series B, Statistical Methodology
https://www.readbyqxmd.com/read/25663813/marginally-specified-priors-for-non-parametric-bayesian-estimation
#15
David C Kessler, Peter D Hoff, David B Dunson
Prior specification for non-parametric 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 for non-parametric Bayes inference in which the prior distribution for a possibly infinite dimensional parameter is decomposed into two parts: an informative prior on a finite set of functionals, and a non-parametric conditional prior for the parameter given the functionals...
January 1, 2015: Journal of the Royal Statistical Society. Series B, Statistical Methodology
https://www.readbyqxmd.com/read/25642139/variance-function-partially-linear-single-index-models-1
#16
Heng Lian, Hua Liang, Raymond J Carroll
We 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 methods for the variance function and for the mean function. Asymptotic theory for the parametric and nonparametric parts of the model is developed...
January 1, 2015: Journal of the Royal Statistical Society. Series B, Statistical Methodology
https://www.readbyqxmd.com/read/25642138/false-discovery-control-in-large-scale-spatial-multiple-testing
#17
Wenguang Sun, Brian J Reich, T Tony Cai, Michele Guindani, Armin Schwartzman
This article develops a unified theoretical and computational framework for false discovery control in multiple testing of spatial signals. We consider both point-wise and cluster-wise spatial analyses, and derive oracle procedures which optimally control the false discovery rate, false discovery exceedance and false cluster rate, respectively. A data-driven finite approximation strategy is developed to mimic the oracle procedures on a continuous spatial domain. Our multiple testing procedures are asymptotically valid and can be effectively implemented using Bayesian computational algorithms for analysis of large spatial data sets...
January 1, 2015: Journal of the Royal Statistical Society. Series B, Statistical Methodology
https://www.readbyqxmd.com/read/25574152/quantile-regression-adjusting-for-dependent-censoring-from-semi-competing-risks
#18
Ruosha Li, Limin Peng
In this work, we study quantile regression when the response is an event time subject to potentially dependent censoring. We consider the semi-competing 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 because they generally require the censoring time and the event time be independent. By imposing rather mild assumptions on the association structure between the time-to-event response and the censoring time variable, we propose quantile regression procedures, which allow us to garner a comprehensive view of the covariate effects on the event time outcome as well as to examine the informativeness of censoring...
January 2015: Journal of the Royal Statistical Society. Series B, Statistical Methodology
https://www.readbyqxmd.com/read/25013362/structured-functional-additive-regression-in-reproducing-kernel-hilbert-spaces
#19
Hongxiao Zhu, Fang Yao, Hao Helen Zhang
Functional additive models (FAMs) provide a flexible yet simple framework for regressions involving functional predictors. The utilization of data-driven 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 framework for the structure estimation in the context of Reproducing Kernel Hilbert Spaces. The proposed approach takes advantage of the functional principal components which greatly facilitates the implementation and the theoretical analysis...
June 1, 2014: Journal of the Royal Statistical Society. Series B, Statistical Methodology
https://www.readbyqxmd.com/read/24817823/the-joint-graphical-lasso-for-inverse-covariance-estimation-across-multiple-classes
#20
Patrick Danaher, Pei Wang, Daniela M Witten
We consider the problem of estimating multiple related Gaussian graphical models from a high-dimensional 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 maximizing a penalized log likelihood. We employ generalized fused lasso or group lasso penalties, and implement a fast ADMM algorithm to solve the corresponding convex optimization problems...
March 2014: Journal of the Royal Statistical Society. Series B, Statistical Methodology
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