Výsledky vyhledávání - "Statistics and Computing/Statistics Programs"

Monte Carlo statistical methods Autor Robert, Christian P., Casella, George

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Practical Bayesian model evaluation using leave-one-out cross-validation and WAIC Autor 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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Statistical tools for nonlinear regression : a practical guide with S-PLUS examples Autor Huet, Sylvie, Bouvier, Annie, Gruet, Marie-Anne, Jolivet, E. (Emmanuel)

“…Statistical Tools for Nonlinear Regression, (Second Edition), presents methods for analyzing data using parametric nonlinear regression models. The new edition…”
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Dynamic nested sampling: an improved algorithm for parameter estimation and evidence calculation Autor 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 Autor 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 Autor 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 Autor 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 Autor 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 Autor 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 Autor 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 Autor 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 Autor 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 Autor 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 Autor 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 Autor 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 Autor 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 Autor 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 Autor Qin, Shanshan, Tan, Zhenni, Wei, Dongwei, Wu, Yuehua

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Deep Gaussian mixture models Autor 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 Autor 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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