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Correction to: Autoregressive optimal transport models. J. R. Stat. Soc. B (IF 5.8) Pub Date : 2023-05-31
[This corrects the article DOI: 10.1093/jrsssb/qkad051.].
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Non-parametric inference about mean functionals of non-ignorable non-response data without identifying the joint distribution. J. R. Stat. Soc. B (IF 5.8) Pub Date : 2023-05-08 Wei Li,Wang Miao,Eric Tchetgen Tchetgen
We consider identification and inference about mean functionals of observed covariates and an outcome variable subject to non-ignorable missingness. By leveraging a shadow variable, we establish a necessary and sufficient condition for identification of the mean functional even if the full data distribution is not identified. We further characterize a necessary condition for n-estimability of the mean
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Elastic integrative analysis of randomised trial and real-world data for treatment heterogeneity estimation. J. R. Stat. Soc. B (IF 5.8) Pub Date : 2023-04-06 Shu Yang,Chenyin Gao,Donglin Zeng,Xiaofei Wang
We propose a test-based elastic integrative analysis of the randomised trial and real-world data to estimate treatment effect heterogeneity with a vector of known effect modifiers. When the real-world data are not subject to bias, our approach combines the trial and real-world data for efficient estimation. Utilising the trial design, we construct a test to decide whether or not to use real-world data
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A statistical test to reject the structural interpretation of a latent factor model J. R. Stat. Soc. B (IF 5.8) Pub Date : 2022-11-22 Tyler J. VanderWeele, Stijn Vansteelandt
Factor analysis is often used to assess whether a single univariate latent variable is sufficient to explain most of the covariance among a set of indicators for some underlying construct. When evidence suggests that a single factor is adequate, research often proceeds by using a univariate summary of the indicators in subsequent research. Implicit in such practices is the assumption that it is the
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Causal inference with spatio-temporal data: Estimating the effects of airstrikes on insurgent violence in Iraq J. R. Stat. Soc. B (IF 5.8) Pub Date : 2022-11-22 Georgia Papadogeorgou, Kosuke Imai, Jason Lyall, Fan Li
Many causal processes have spatial and temporal dimensions. Yet the classic causal inference framework is not directly applicable when the treatment and outcome variables are generated by spatio-temporal point processes. We extend the potential outcomes framework to these settings by formulating the treatment point process as a stochastic intervention. Our causal estimands include the expected number
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High-dimensional principal component analysis with heterogeneous missingness J. R. Stat. Soc. B (IF 5.8) Pub Date : 2022-11-20 Ziwei Zhu, Tengyao Wang, Richard J. Samworth
We study the problem of high-dimensional Principal Component Analysis (PCA) with missing observations. In a simple, homogeneous observation model, we show that an existing observed-proportion weighted (OPW) estimator of the leading principal components can (nearly) attain the minimax optimal rate of convergence, which exhibits an interesting phase transition. However, deeper investigation reveals that
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Structure learning for extremal tree models J. R. Stat. Soc. B (IF 5.8) Pub Date : 2022-11-18 Sebastian Engelke, Stanislav Volgushev
Extremal graphical models are sparse statistical models for multivariate extreme events. The underlying graph encodes conditional independencies and enables a visual interpretation of the complex extremal dependence structure. For the important case of tree models, we develop a data-driven methodology for learning the graphical structure. We show that sample versions of the extremal correlation and
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Empirical likelihood-based inference for functional means with application to wearable device data J. R. Stat. Soc. B (IF 5.8) Pub Date : 2022-11-16 Hsin-wen Chang, Ian W. McKeague
This paper develops a nonparametric inference framework that is applicable to occupation time curves derived from wearable device data. These curves consider all activity levels within the range of device readings, which is preferable to the practice of classifying activity into discrete categories. Motivated by certain features of these curves, we introduce a powerful likelihood ratio approach to
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ZAP: Z$$ Z $$-value adaptive procedures for false discovery rate control with side information J. R. Stat. Soc. B (IF 5.8) Pub Date : 2022-11-02 Dennis Leung, Wenguang Sun
Adaptive multiple testing with covariates is an important research direction that has gained major attention in recent years. It has been widely recognised that leveraging side information provided by auxiliary covariates can improve the power of false discovery rate (FDR) procedures. Currently, most such procedures are devised with p-values as their main statistics. However, for two-sided hypotheses
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Conditional independence testing in Hilbert spaces with applications to functional data analysis J. R. Stat. Soc. B (IF 5.8) Pub Date : 2022-11-02 Anton Rask Lundborg, Rajen D. Shah, Jonas Peters
We study the problem of testing the null hypothesis that X and Y are conditionally independent given Z, where each of X, Y and Z may be functional random variables. This generalises testing the significance of X in a regression model of scalar response Y on functional regressors X and Z. We show, however, that even in the idealised setting where additionally (X, Y, Z) has a Gaussian distribution, the
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Linear regression and its inference on noisy network-linked data J. R. Stat. Soc. B (IF 5.8) Pub Date : 2022-11-02 Can M. Le, Tianxi Li
Linear regression on network-linked observations has been an essential tool in modelling the relationship between response and covariates with additional network structures. Previous methods either lack inference tools or rely on restrictive assumptions on social effects and usually assume that networks are observed without errors. This paper proposes a regression model with non-parametric network
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Dimension-free mixing for high-dimensional Bayesian variable selection J. R. Stat. Soc. B (IF 5.8) Pub Date : 2022-10-31 Quan Zhou, Jun Yang, Dootika Vats, Gareth O. Roberts, Jeffrey S. Rosenthal
Yang et al. proved that the symmetric random walk Metropolis–Hastings algorithm for Bayesian variable selection is rapidly mixing under mild high-dimensional assumptions. We propose a novel Markov chain Monte Carlo (MCMC) sampler using an informed proposal scheme, which we prove achieves a much faster mixing time that is independent of the number of covariates, under the assumptions of Yang et al.
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CovNet: Covariance networks for functional data on multidimensional domains J. R. Stat. Soc. B (IF 5.8) Pub Date : 2022-10-31 Soham Sarkar, Victor M. Panaretos
Covariance estimation is ubiquitous in functional data analysis. Yet, the case of functional observations over multidimensional domains introduces computational and statistical challenges, rendering the standard methods effectively inapplicable. To address this problem, we introduce Covariance Networks (CovNet) as a modelling and estimation tool. The CovNet model is universal—it can be used to approximate
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An approximation algorithm for blocking of an experimental design J. R. Stat. Soc. B (IF 5.8) Pub Date : 2022-10-31 Bikram Karmakar
Blocked randomized designs are used to improve the precision of treatment effect estimates compared to a completely randomized design. A block is a set of units that are relatively homogeneous and consequently would tend to produce relatively similar outcomes if the treatment had no effect. The problem of finding the optimal blocking of the units into equal sized blocks of any given size larger than
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Segmenting time series via self-normalisation J. R. Stat. Soc. B (IF 5.8) Pub Date : 2022-10-30 Zifeng Zhao, Feiyu Jiang, Xiaofeng Shao
We propose a novel and unified framework for change-point estimation in multivariate time series. The proposed method is fully non-parametric, robust to temporal dependence and avoids the demanding consistent estimation of long-run variance. One salient and distinct feature of the proposed method is its versatility, where it allows change-point detection for a broad class of parameters (such as mean
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Exact clustering in tensor block model: Statistical optimality and computational limit J. R. Stat. Soc. B (IF 5.8) Pub Date : 2022-10-30 Rungang Han, Yuetian Luo, Miaoyan Wang, Anru R. Zhang
High-order clustering aims to identify heterogeneous substructures in multiway datasets that arise commonly in neuroimaging, genomics, social network studies, etc. The non-convex and discontinuous nature of this problem pose significant challenges in both statistics and computation. In this paper, we propose a tensor block model and the computationally efficient methods, high-order Lloyd algorithm
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General Bayesian loss function selection and the use of improper models J. R. Stat. Soc. B (IF 5.8) Pub Date : 2022-10-25 Jack Jewson, David Rossell
Statisticians often face the choice between using probability models or a paradigm defined by minimising a loss function. Both approaches are useful and, if the loss can be re-cast into a proper probability model, there are many tools to decide which model or loss is more appropriate for the observed data, in the sense of explaining the data's nature. However, when the loss leads to an improper model
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Universal prediction band via semi-definite programming J. R. Stat. Soc. B (IF 5.8) Pub Date : 2022-08-19 Tengyuan Liang
We propose a computationally efficient method to construct nonparametric, heteroscedastic prediction bands for uncertainty quantification, with or without any user-specified predictive model. Our approach provides an alternative to the now-standard conformal prediction for uncertainty quantification, with novel theoretical insights and computational advantages. The data-adaptive prediction band is
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Calibrating the scan statistic: Finite sample performance versus asymptotics J. R. Stat. Soc. B (IF 5.8) Pub Date : 2022-08-16 Guenther Walther, Andrew Perry
We consider the problem of detecting an elevated mean on an interval with unknown location and length in the univariate Gaussian sequence model. Recent results have shown that using scale-dependent critical values for the scan statistic allows to attain asymptotically optimal detection simultaneously for all signal lengths, thereby improving on the traditional scan, but this procedure has been criticised
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Assumption-lean inference for generalised linear model parameters J. R. Stat. Soc. B (IF 5.8) Pub Date : 2022-07-26 Stijn Vansteelandt, Oliver Dukes
Inference for the parameters indexing generalised linear models is routinely based on the assumption that the model is correct and a priori specified. This is unsatisfactory because the chosen model is usually the result of a data-adaptive model selection process, which may induce excess uncertainty that is not usually acknowledged. Moreover, the assumptions encoded in the chosen model rarely represent
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Proposer of the vote of thanks and contribution to the Discussion of ‘Assumption-lean inference for generalised linear model parameters’ by Vansteelandt and Dukes J. R. Stat. Soc. B (IF 5.8) Pub Date : 2022-07-26 Rhian M. Daniel
TWO CONTRASTING PHILOSOPHIES Traditional statistical modelling starts from a family F of observed data laws indexed by unknown parameters of interest β. The goal is to make inference about β under the assumption that F contains the true law. By labelling β ‘of interest’, it is implied that F can be expressed such that β naturally encompasses the main scientific goal, which is not always the case. Furthermore
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Seconder of the vote of thanks to Vansteelandt and Dukes and contribution to the Discussion of ‘Assumption-lean inference for generalised linear model parameters’ J. R. Stat. Soc. B (IF 5.8) Pub Date : 2022-07-26 Vanessa Didelez
In my view, one of the most important contributions of the field of causal inference has been to place the target of inference, the desired estimand, at the centre of the analysis. The estimand is chosen in view of the research question, and typically reflects what decision problem we need to solve or what our ideal (target) trial would be. Crucially, the (causal) estimand is not automatically a parameter
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Mats J Stensrud and Aaron L. Sarvet’s contribution to the Discussion of ‘Assumption-lean inference for generalised linear model parameters’ by Vansteelandt and Dukes J. R. Stat. Soc. B (IF 5.8) Pub Date : 2022-07-26 Mats J. Stensrud, Aaron L. Sarvet
We congratulate Vansteelandt and Dukes (V & D) with their innovative and interesting article. Here we further explore the interpretation of V & D’s main effects estimand. An algorithm for causal inference. There has been tremendous progress in causal inference by approaching causal queries in the following way (Hernan & Robins, 2020; Richardson & Robins, 2013; Robins, 1986): Choose a causal target:
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Christian Hennig's contribution to the Discussion of ‘Assumption-lean inference for generalised linear model parameters’ by Vansteelandt and Dukes J. R. Stat. Soc. B (IF 5.8) Pub Date : 2022-07-26 Christian Hennig
There is a tendency in statistics to talk about model assumptions in a misleading way. Most of us probably agree with George Box's ‘all models are wrong but some are useful’, yet there is much communication that implies that for applying methods ‘assuming’ certain models, these models have to be true. If this were so, no model-based method could ever be used! Generally model assumptions do not have
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Chaohua Dong, Jiti Gao and Oliver Linton’s contribution to the Discussion of ‘Assumption-lean inference for generalised linear model parameters’ by Vansteelandt and Dukes J. R. Stat. Soc. B (IF 5.8) Pub Date : 2022-07-26 Chaohua Dong, Jiti Gao, Oliver Linton
The title of this paper is ironically self-fulfilling, since there are almost no meaningful assumptions made throughout! The starting point is that we have some plausible semi-parametric model, which is a special case of a more general non-parametric model, but we wish to allow for misspecification and in particular define an estimand that is meaningful in the non-parametric model and that specialises
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Ian Hunt's contribution to the Discussion of ‘Assumption-lean inference for generalised linear model parameters’ by Vansteelandt and Dukes J. R. Stat. Soc. B (IF 5.8) Pub Date : 2022-07-26 Ian Hunt
This paper offers a generic and principled addition to conventional statistical modelling. It is a real advance in applicable methodology. But there is no need in the paper for the following claims: that statisticians routinely use ‘dishonest’ modelling assumptions, that statisticians should aim for ‘purely evidence-based’ inferences and that modelling assumptions are ‘almost always a pure mathematical
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Kuldeep Kumar’s contribution to the Discussion of ‘Assumption-lean inference for generalised linear model parameters’ by Vansteelandt and Dukes J. R. Stat. Soc. B (IF 5.8) Pub Date : 2022-07-26 Kuldeep Kumar
According to Leo Breiman (2001), there are two broad cultures for analysing and modelling to reach conclusions from the data. The first one is data modelling culture where the value of the parameters are estimated from the data and then the model is used for information and/or prediction. The second one is algorithmic modelling culture, where the approach is to find a function f(x) using an algorithm
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Michael Lavine and James Hodges’ contribution to the Discussion of ‘Assumption-lean inference for generalised linear model parameters’ by Vansteelandt and Dukes J. R. Stat. Soc. B (IF 5.8) Pub Date : 2022-07-26 Michael Lavine, James Hodges
The authors advocate a style and rhetoric of statistical analysis that begins with specifying an estimand and ends with an estimate and interval. They say, ‘The starting point… is to come up with an estimand that is meaningful when the above [generalized partially linear] model [(4)] does not hold, but reduces to [the familiar regression coefficient] β when the model holds; this… allows for nonparametric
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Elizabeth L Ogburn, Junhui Cai, Arun K Kuchibhotla, Richard A Berk and Andreas Buja’s contribution to the Discussion of ‘Assumption-lean inference for generalised linear model parameters’ by Vansteelandt and Dukes J. R. Stat. Soc. B (IF 5.8) Pub Date : 2022-07-26 Elizabeth L. Ogburn, Junhui Cai, Arun K. Kuchibhotla, Richard A. Berk, Andreas Buja
Not all conditional associations between outcomes and exposures are of interest. Those that are tend to be directional: up or down. The simplest way to assess directionality is to fit a confounder- adjusted linear exposure term, as the authors propose. We agree with this approach as some of us have argued that linear slopes are meaningful and interpretable even if the directional association is not
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Ilya Shpitser’s contribution to the Discussion of ‘Assumption-lean inference for generalised linear model parameters’ by Vansteelandt and Dukes J. R. Stat. Soc. B (IF 5.8) Pub Date : 2022-07-26 Ilya Shpitser
A hallmark of principled causal inference is being careful and explicit about assumptions underlying data analysis. In their timely paper, the authors, Professor Stijn Vansteelandt and Oliver Dukes, adopt this view to provide a general roadmap for cautious statistical inference about target parameters. The authors distinguish between assumptions made for substantive reasons and those made for convenience
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Authors' reply to the Discussion of ‘Assumption-lean inference for generalised linear model parameters’ by Vansteelandt and Dukes J. R. Stat. Soc. B (IF 5.8) Pub Date : 2022-07-26 Stijn Vansteelandt, Oliver Dukes
We thank all discussants for their interesting and thoughtful comments on our paper. In this rejoinder, we will focus on common themes amongst the commentaries and will close with a discussion of some open issues.
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The Debiased Spatial Whittle likelihood J. R. Stat. Soc. B (IF 5.8) Pub Date : 2022-07-20 Arthur P. Guillaumin, Adam M. Sykulski, Sofia C. Olhede, Frederik J. Simons
We provide a computationally and statistically efficient method for estimating the parameters of a stochastic covariance model observed on a regular spatial grid in any number of dimensions. Our proposed method, which we call the Debiased Spatial Whittle likelihood, makes important corrections to the well-known Whittle likelihood to account for large sources of bias caused by boundary effects and aliasing
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High-dimensional changepoint estimation with heterogeneous missingness J. R. Stat. Soc. B (IF 5.8) Pub Date : 2022-07-11 Bertille Follain, Tengyao Wang, Richard J. Samworth
We propose a new method for changepoint estimation in partially observed, high-dimensional time series that undergo a simultaneous change in mean in a sparse subset of coordinates. Our first methodological contribution is to introduce a ‘MissCUSUM’ transformation (a generalisation of the popular cumulative sum statistics), that captures the interaction between the signal strength and the level of missingness
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Paired or partially paired two-sample tests with unordered samples J. R. Stat. Soc. B (IF 5.8) Pub Date : 2022-06-21 Yudong Wang, Yanlin Tang, Zhi-Sheng Ye
In paired two-sample tests for mean equality, it is common to encounter unordered samples in which subject identities are not observed or unobservable, and it is impossible to link the measurements before and after treatment. The absence of subject identities masks the correspondence between the two samples, rendering existing methods inapplicable. In this paper, we propose two novel testing approaches
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On the cross-validation bias due to unsupervised preprocessing J. R. Stat. Soc. B (IF 5.8) Pub Date : 2022-06-14 Amit Moscovich, Saharon Rosset
Cross-validation is the de facto standard for predictive model evaluation and selection. In proper use, it provides an unbiased estimate of a model's predictive performance. However, data sets often undergo various forms of data-dependent preprocessing, such as mean-centring, rescaling, dimensionality reduction and outlier removal. It is often believed that such preprocessing stages, if done in an
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A statistical interpretation of spectral embedding: The generalised random dot product graph J. R. Stat. Soc. B (IF 5.8) Pub Date : 2022-06-03 Patrick Rubin-Delanchy, Joshua Cape, Minh Tang, Carey E. Priebe
Spectral embedding is a procedure which can be used to obtain vector representations of the nodes of a graph. This paper proposes a generalisation of the latent position network model known as the random dot product graph, to allow interpretation of those vector representations as latent position estimates. The generalisation is needed to model heterophilic connectivity (e.g. ‘opposites attract’) and
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Functional peaks-over-threshold analysis J. R. Stat. Soc. B (IF 5.8) Pub Date : 2022-05-20 Raphaël de Fondeville, Anthony C. Davison
Peaks-over-threshold analysis using the generalised Pareto distribution is widely applied in modelling tails of univariate random variables, but much information may be lost when complex extreme events are studied using univariate results. In this paper, we extend peaks-over-threshold analysis to extremes of functional data. Threshold exceedances defined using a functional r are modelled by the generalised
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Multiply robust estimation of causal effects under principal ignorability J. R. Stat. Soc. B (IF 5.8) Pub Date : 2022-05-20 Zhichao Jiang, Shu Yang, Peng Ding
Causal inference concerns not only the average effect of the treatment on the outcome but also the underlying mechanism through an intermediate variable of interest. Principal stratification characterizes such a mechanism by targeting subgroup causal effects within principal strata, which are defined by the joint potential values of an intermediate variable. Due to the fundamental problem of causal
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Corrigendum to ‘Simulation of multivariate diffusion bridges’ J. R. Stat. Soc. B (IF 5.8) Pub Date : 2022-04-26 Mogens Bladt, Samuel Finch, Michael Sørensen
We correct an error in Theorem 1 in Bladt et al. (2016) Journal of the Royal Statistical Society: Series B, 78, 343–369 by changing the initial distribution of an auxiliary diffusion process, which is used to describe the distribution of the proposed approximate diffusion bridges. As a consequence, we correct two algorithms for simulating exact diffusion bridges by changing the initial distribution
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Efficient evaluation of prediction rules in semi-supervised settings under stratified sampling J. R. Stat. Soc. B (IF 5.8) Pub Date : 2022-04-26 Jessica Gronsbell, Molei Liu, Lu Tian, Tianxi Cai
In many contemporary applications, large amounts of unlabelled data are readily available while labelled examples are limited. There has been substantial interest in semi-supervised learning (SSL) which aims to leverage unlabelled data to improve estimation or prediction. However, current SSL literature focuses primarily on settings where labelled data are selected uniformly at random from the population
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Nonparametric, tuning-free estimation of S-shaped functions J. R. Stat. Soc. B (IF 5.8) Pub Date : 2022-04-21 Oliver Y. Feng, Yining Chen, Qiyang Han, Raymond J. Carroll, Richard J. Samworth
We consider the nonparametric estimation of an S-shaped regression function. The least squares estimator provides a very natural, tuning-free approach, but results in a non-convex optimization problem, since the inflection point is unknown. We show that the estimator may nevertheless be regarded as a projection onto a finite union of convex cones, which allows us to propose a mixed primal-dual bases
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Bayesian inference for risk minimization via exponentially tilted empirical likelihood J. R. Stat. Soc. B (IF 5.8) Pub Date : 2022-04-20 Rong Tang, Yun Yang
The celebrated Bernstein von-Mises theorem ensures credible regions from a Bayesian posterior to be well-calibrated when the model is correctly-specified, in the frequentist sense that their coverage probabilities tend to the nominal values as data accrue. However, this conventional Bayesian framework is known to lack robustness when the model is misspecified or partly specified, for example, in quantile
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Bayesian context trees: Modelling and exact inference for discrete time series J. R. Stat. Soc. B (IF 5.8) Pub Date : 2022-04-20 Ioannis Kontoyiannis, Lambros Mertzanis, Athina Panotopoulou, Ioannis Papageorgiou, Maria Skoularidou
We develop a new Bayesian modelling framework for the class of higher-order, variable-memory Markov chains, and introduce an associated collection of methodological tools for exact inference with discrete time series. We show that a version of the context tree weighting alg-orithm can compute the prior predictive likelihood exa-ctly (averaged over both models and parameters), and two related algorithms
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Manifold Markov chain Monte Carlo methods for Bayesian inference in diffusion models J. R. Stat. Soc. B (IF 5.8) Pub Date : 2022-04-19 Matthew M. Graham, Alexandre H. Thiery, Alexandros Beskos
Bayesian inference for nonlinear diffusions, observed at discrete times, is a challenging task that has prompted the development of a number of algorithms, mainly within the computational statistics community. We propose a new direction, and accompanying methodology—borrowing ideas from statistical physics and computational chemistry—for inferring the posterior distribution of latent diffusion paths
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Fast increased fidelity samplers for approximate Bayesian Gaussian process regression J. R. Stat. Soc. B (IF 5.8) Pub Date : 2022-04-19 Kelly R. Moran, Matthew W. Wheeler
Gaussian processes (GPs) are common components in Bayesian non-parametric models having a rich methodological literature and strong theoretical grounding. The use of exact GPs in Bayesian models is limited to problems containing several thousand observations due to their prohibitive computational demands. We develop a posterior sampling algorithm using H-matrix approximations that scales at O(nlog2n)
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Semiparametric latent class analysis of recurrent event data J. R. Stat. Soc. B (IF 5.8) Pub Date : 2022-04-14 Wei Zhao, Limin Peng, John Hanfelt
Recurrent event data frequently arise in chronic disease studies, providing rich information on disease progression. The concept of latent class offers a sensible perspective to characterize complex population heterogeneity in recurrent event trajectories that may not be adequately captured by a single regression model. However, the development of latent class methods for recurrent event data has been
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Bootstrap inference for the finite population mean under complex sampling designs J. R. Stat. Soc. B (IF 5.8) Pub Date : 2022-04-13 Zhonglei Wang, Liuhua Peng, Jae Kwang Kim
Bootstrap is a useful computational tool for statistical inference, but it may lead to erroneous analysis under complex survey sampling. In this paper, we propose a unified bootstrap method for stratified multi-stage cluster sampling, Poisson sampling, simple random sampling without replacement and probability proportional to size sampling with replacement. In the proposed bootstrap method, we first
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Efficient manifold approximation with spherelets J. R. Stat. Soc. B (IF 5.8) Pub Date : 2022-04-12 Didong Li, Minerva Mukhopadhyay, David B. Dunson
In statistical dimensionality reduction, it is common to rely on the assumption that high dimensional data tend to concentrate near a lower dimensional manifold. There is a rich literature on approximating the unknown manifold, and on exploiting such approximations in clustering, data compression, and prediction. Most of the literature relies on linear or locally linear approximations. In this article
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Testing for a change in mean after changepoint detection J. R. Stat. Soc. B (IF 5.8) Pub Date : 2022-04-12 Sean Jewell, Paul Fearnhead, Daniela Witten
While many methods are available to detect structural changes in a time series, few procedures are available to quantify the uncertainty of these estimates post-detection. In this work, we fill this gap by proposing a new framework to test the null hypothesis that there is no change in mean around an estimated changepoint. We further show that it is possible to efficiently carry out this framework
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Optimal and maximin procedures for multiple testing problems J. R. Stat. Soc. B (IF 5.8) Pub Date : 2022-04-12 Saharon Rosset, Ruth Heller, Amichai Painsky, Ehud Aharoni
Multiple testing problems (MTPs) are a staple of modern statistical analysis. The fundamental objective of MTPs is to reject as many false null hypotheses as possible (that is, maximize some notion of power), subject to controlling an overall measure of false discovery, like family-wise error rate (FWER) or false discovery rate (FDR). In this paper we provide generalizations to MTPs of the optimal
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Robust generalised Bayesian inference for intractable likelihoods J. R. Stat. Soc. B (IF 5.8) Pub Date : 2022-04-03 Takuo Matsubara, Jeremias Knoblauch, François-Xavier Briol, Chris J. Oates
Generalised Bayesian inference updates prior beliefs using a loss function, rather than a likelihood, and can therefore be used to confer robustness against possible mis-specification of the likelihood. Here we consider generalised Bayesian inference with a Stein discrepancy as a loss function, motivated by applications in which the likelihood contains an intractable normalisation constant. In this
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Optimal thinning of MCMC output J. R. Stat. Soc. B (IF 5.8) Pub Date : 2022-04-03 Marina Riabiz, Wilson Ye Chen, Jon Cockayne, Pawel Swietach, Steven A. Niederer, Lester Mackey, Chris. J. Oates
The use of heuristics to assess the convergence and compress the output of Markov chain Monte Carlo can be sub-optimal in terms of the empirical approximations that are produced. Typically a number of the initial states are attributed to ‘burn in’ and removed, while the remainder of the chain is ‘thinned’ if compression is also required. In this paper, we consider the problem of retrospectively selecting
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SIMPLE: Statistical inference on membership profiles in large networks J. R. Stat. Soc. B (IF 5.8) Pub Date : 2022-03-30 Jianqing Fan, Yingying Fan, Xiao Han, Jinchi Lv
Network data are prevalent in many contemporary big data applications in which a common interest is to unveil important latent links between different pairs of nodes. Yet a simple fundamental question of how to precisely quantify the statistical uncertainty associated with the identification of latent links still remains largely unexplored. In this paper, we propose the method of statistical inference
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Coupling-based convergence assessment of some Gibbs samplers for high-dimensional Bayesian regression with shrinkage priors J. R. Stat. Soc. B (IF 5.8) Pub Date : 2022-03-28 Niloy Biswas, Anirban Bhattacharya, Pierre E. Jacob, James E. Johndrow
We consider Markov chain Monte Carlo (MCMC) algorithms for Bayesian high-dimensional regression with continuous shrinkage priors. A common challenge with these algorithms is the choice of the number of iterations to perform. This is critical when each iteration is expensive, as is the case when dealing with modern data sets, such as genome-wide association studies with thousands of rows and up to hundreds
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Graphical criteria for efficient total effect estimation via adjustment in causal linear models J. R. Stat. Soc. B (IF 5.8) Pub Date : 2022-03-21 Leonard Henckel, Emilija Perković, Marloes H. Maathuis
Covariate adjustment is a commonly used method for total causal effect estimation. In recent years, graphical criteria have been developed to identify all valid adjustment sets, that is, all covariate sets that can be used for this purpose. Different valid adjustment sets typically provide total causal effect estimates of varying accuracies. Restricting ourselves to causal linear models, we introduce
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Functional structural equation model J. R. Stat. Soc. B (IF 5.8) Pub Date : 2022-03-21 Kuang-Yao Lee, Lexin Li
In this article, we introduce a functional structural equation model for estimating directional relations from multivariate functional data. We decouple the estimation into two major steps: directional order determination and selection through sparse functional regression. We first propose a score function at the linear operator level, and show that its minimization can recover the true directional
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A kernel-expanded stochastic neural network J. R. Stat. Soc. B (IF 5.8) Pub Date : 2022-03-17 Yan Sun, Faming Liang
The deep neural network suffers from many fundamental issues in machine learning. For example, it often gets trapped into a local minimum in training, and its prediction uncertainty is hard to be assessed. To address these issues, we propose the so-called kernel-expanded stochastic neural network (K-StoNet) model, which incorporates support vector regression as the first hidden layer and reformulates
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On functional processes with multiple discontinuities J. R. Stat. Soc. B (IF 5.8) Pub Date : 2022-03-09 Jialiang Li, Yaguang Li, Tailen Hsing
We consider the problem of estimating multiple change points for a functional data process. There are numerous examples in science and finance in which the process of interest may be subject to some sudden changes in the mean. The process data that are not in a close vicinity of any change point can be analysed by the usual nonparametric smoothing methods. However, the data close to change points and
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Supervised multivariate learning with simultaneous feature auto-grouping and dimension reduction J. R. Stat. Soc. B (IF 5.8) Pub Date : 2022-03-02 Yiyuan She, Jiahui Shen, Chao Zhang
Modern high-dimensional methods often adopt the ‘bet on sparsity’ principle, while in supervised multivariate learning statisticians may face ‘dense’ problems with a large number of nonzero coefficients. This paper proposes a novel clustered reduced-rank learning (CRL) framework that imposes two joint matrix regularizations to automatically group the features in constructing predictive factors. CRL
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Gaussian differential privacy J. R. Stat. Soc. B (IF 5.8) Pub Date : 2022-02-21 Jinshuo Dong, Aaron Roth, Weijie J. Su
In the past decade, differential privacy has seen remarkable success as a rigorous and practical formalization of data privacy. This privacy definition and its divergence based relaxations, however, have several acknowledged weaknesses, either in handling composition of private algorithms or in analysing important primitives like privacy amplification by subsampling. Inspired by the hypothesis testing