Search Results - "Statistics and Computing/Statistics Programs"

Practical Bayesian model evaluation using leave-one-out cross-validation and WAIC by Vehtari, Aki, Gelman, Andrew, Gabry, Jonah

“…Leave-one-out cross-validation (LOO) and the widely applicable information criterion (WAIC) are methods for estimating pointwise out-of-sample prediction…”
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Dynamic nested sampling: an improved algorithm for parameter estimation and evidence calculation by Higson, Edward, Handley, Will, Hobson, Michael, Lasenby, Anthony

“…We introduce dynamic nested sampling: a generalisation of the nested sampling algorithm in which the number of “live points” varies to allocate samples more…”
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Correlation and variable importance in random forests by Gregorutti, Baptiste, Michel, Bertrand, Saint-Pierre, Philippe

“…This paper is about variable selection with the random forests algorithm in presence of correlated predictors. In high-dimensional regression or classification…”
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Understanding predictive information criteria for Bayesian models by Gelman, Andrew, Hwang, Jessica, Vehtari, Aki

“…We review the Akaike, deviance, and Watanabe-Akaike information criteria from a Bayesian perspective, where the goal is to estimate expected…”
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Hilbert space methods for reduced-rank Gaussian process regression by Solin, Arno, Särkkä, Simo

“…This paper proposes a novel scheme for reduced-rank Gaussian process regression. The method is based on an approximate series expansion of the covariance…”
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Unrestricted permutation forces extrapolation: variable importance requires at least one more model, or there is no free variable importance by Hooker, Giles, Mentch, Lucas, Zhou, Siyu

“…This paper reviews and advocates against the use of permute-and-predict (PaP) methods for interpreting black box functions. Methods such as the variable…”
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A comparison of zero-inflated and hurdle models for modeling zero-inflated count data by Feng, Cindy Xin

“…Counts data with excessive zeros are frequently encountered in practice. For example, the number of health services visits often includes many zeros…”
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A note on using the F-measure for evaluating record linkage algorithms by Hand, David, Christen, Peter

“…Record linkage is the process of identifying and linking records about the same entities from one or more databases. Record linkage can be viewed as a…”
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Comparison of Bayesian predictive methods for model selection by Piironen, Juho, Vehtari, Aki

“…The goal of this paper is to compare several widely used Bayesian model selection methods in practical model selection problems, highlight their differences…”
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A statistical test for Nested Sampling algorithms by Buchner, Johannes

“…Nested sampling is an iterative integration procedure that shrinks the prior volume towards higher likelihoods by removing a “live” point at a time. A…”
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Bayesian learning via neural Schrödinger–Föllmer flows by Vargas, Francisco, Ovsianas, Andrius, Fernandes, David, Girolami, Mark, Lawrence, Neil D., Nüsken, Nikolas

“…In this work we explore a new framework for approximate Bayesian inference in large datasets based on stochastic control. We advocate stochastic control as a…”
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Mean and median bias reduction in generalized linear models by Kosmidis, Ioannis, Kenne Pagui, Euloge Clovis, Sartori, Nicola

“…This paper presents an integrated framework for estimation and inference from generalized linear models using adjusted score equations that result in mean and…”
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Approximate Bayesian computational methods by Marin, Jean-Michel, Pudlo, Pierre, Robert, Christian P., Ryder, Robin J.

“…Approximate Bayesian Computation (ABC) methods, also known as likelihood-free techniques, have appeared in the past ten years as the most satisfactory approach…”
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On optimal multiple changepoint algorithms for large data by Maidstone, Robert, Hocking, Toby, Rigaill, Guillem, Fearnhead, Paul

“…Many common approaches to detecting changepoints, for example based on statistical criteria such as penalised likelihood or minimum description length, can be…”
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Learning Bayesian networks from big data with greedy search: computational complexity and efficient implementation by Scutari, Marco, Vitolo, Claudia, Tucker, Allan

“…Learning the structure of Bayesian networks from data is known to be a computationally challenging, NP-hard problem. The literature has long investigated how…”
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Correction: PCA-uCPD: an ensemble method for multiple change-point detection in moderately high-dimensional data by Qin, Shanshan, Tan, Zhenni, Wei, Dongwei, Wu, Yuehua

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Deep Gaussian mixture models by Viroli, Cinzia, McLachlan, Geoffrey J.

“…Deep learning is a hierarchical inference method formed by subsequent multiple layers of learning able to more efficiently describe complex relationships. In…”
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Shape constrained additive models by Pya, Natalya, Wood, Simon N.

“…A framework is presented for generalized additive modelling under shape constraints on the component functions of the linear predictor of the GAM. We represent…”
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Estimation of prediction error by using K-fold cross-validation by Fushiki, Tadayoshi

“…Estimation of prediction accuracy is important when our aim is prediction. The training error is an easy estimate of prediction error, but it has a downward…”
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Group descent algorithms for nonconvex penalized linear and logistic regression models with grouped predictors by Breheny, Patrick, Huang, Jian

“…Penalized regression is an attractive framework for variable selection problems. Often, variables possess a grouping structure, and the relevant selection…”
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