Výsledky vyhledávání - "Journal of the Royal Statistical Society. Series B, Statistical methodology"

Visualizing the effects of predictor variables in black box supervised learning models Autor Apley, Daniel W., Zhu, Jingyu

“…In many supervised learning applications, understanding and visualizing the effects of the predictor variables on the predicted response is of paramount…”
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Gaussian differential privacy Autor Dong, Jinshuo, Roth, Aaron, Su, Weijie J.

“…In the past decade, differential privacy has seen remarkable success as a rigorous and practical formalization of data privacy. This privacy definition and its…”
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Making sense of sensitivity: extending omitted variable bias Autor Cinelli, Carlos, Hazlett, Chad

“…We extend the omitted variable bias framework with a suite of tools for sensitivity analysis in regression models that does not require assumptions on the…”
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Panning for gold: ‘model-X’ knockoffs for high dimensional controlled variable selection Autor Candès, Emmanuel, Fan, Yingying, Janson, Lucas, Lv, Jinchi

“…Many contemporary large-scale applications involve building interpretable models linking a large set of potential covariates to a response in a non-linear…”
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Transfer learning for high‐dimensional linear regression: Prediction, estimation and minimax optimality Autor Li, Sai, Cai, T. Tony, Li, Hongzhe

“…This paper considers estimation and prediction of a high‐dimensional linear regression in the setting of transfer learning where, in addition to observations…”
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Causal inference by using invariant prediction: identification and confidence intervals Autor Peters, Jonas, Bühlmann, Peter, Meinshausen, Nicolai

“…What is the difference between a prediction that is made with a causal model and that with a non-causal model? Suppose that we intervene on the predictor…”
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Approximate residual balancing: debiased inference of average treatment effects in high dimensions Autor Athey, Susan, Imbens, Guido W., Wager, Stefan

“…There are many settings where researchers are interested in estimating average treatment effects and are willing to rely on the unconfoundedness assumption,…”
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A general framework for updating belief distributions Autor Bissiri, P. G., Holmes, C. C., Walker, S. G.

“…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…”
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Graphical models for extremes Autor Engelke, Sebastian, Hitz, Adrien S.

“…Conditional independence, graphical models and sparsity are key notions for parsimonious statistical models and for understanding the structural relationships…”
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Statistical clustering of temporal networks through a dynamic stochastic block model Autor Matias, Catherine, Miele, Vincent

“…Statistical node clustering in discrete time dynamic networks is an emerging field that raises many challenges. Here, we explore statistical properties and…”
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Non‐reversible parallel tempering: A scalable highly parallel MCMC scheme Autor Syed, Saifuddin, Bouchard‐Côté, Alexandre, Deligiannidis, George, Doucet, Arnaud

“…Parallel tempering (PT) methods are a popular class of Markov chain Monte Carlo schemes used to sample complex high‐dimensional probability distributions. They…”
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Covariate balancing propensity score Autor Imai, Kosuke, Ratkovic, Marc

“…The propensity score plays a central role in a variety of causal inference settings. In particular, matching and weighting methods based on the estimated…”
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Confidence intervals for low dimensional parameters in high dimensional linear models Autor Zhang, Cun‐Hui, Zhang, Stephanie S

“…The purpose of this paper is to propose methodologies for statistical inference of low dimensional parameters with high dimensional data. We focus on…”
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joint graphical lasso for inverse covariance estimation across multiple classes Autor Danaher, Patrick, Wang, Pei, Witten, Daniela M

“…We consider the problem of estimating multiple related Gaussian graphical models from a high dimensional data set with observations belonging to distinct…”
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Approximate Bayesian computation with the Wasserstein distance Autor Bernton, Espen, Jacob, Pierre E., Gerber, Mathieu, Robert, Christian P.

“…A growing number of generative statistical models do not permit the numerical evaluation of their likelihood functions. Approximate Bayesian computation has…”
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Sparse graphs using exchangeable random measures Autor Caron, François, Fox, Emily B.

“…Statistical network modelling has focused on representing the graph as a discrete structure, namely the adjacency matrix. When assuming exchangeability of this…”
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Kernel-based tests for joint independence Autor Pfister, Niklas, Bühlmann, Peter, Schölkopf, Bernhard, Peters, Jonas

“…We investigate the problem of testing whether d possibly multivariate random variables, which may or may not be continuous, are jointly (or mutually)…”
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Multiple‐change‐point detection for high dimensional time series via sparsified binary segmentation Autor Cho, Haeran, Fryzlewicz, Piotr

“…Time series segmentation, which is also known as multiple‐change‐point detection, is a well‐established problem. However, few solutions have been designed…”
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Large covariance estimation by thresholding principal orthogonal complements Autor Fan, Jianqing, Liao, Yuan, Mincheva, Martina

“…The paper deals with the estimation of a high dimensional covariance with a conditional sparsity structure and fast diverging eigenvalues. By assuming a sparse…”
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Robust estimation via robust gradient estimation Autor Prasad, Adarsh, Suggala, Arun Sai, Balakrishnan, Sivaraman, Ravikumar, Pradeep

“…We provide a new computationally efficient class of estimators for risk minimization. We show that these estimators are robust for general statistical models,…”
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