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Year of publication
Subject
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EM algorithm 47 Bootstrap 37 Variable selection 36 Model selection 35 Markov chain Monte Carlo 34 Maximum likelihood 25 Robustness 24 Simulation 23 Classification 22 Dynamic programming 22 Bayesian inference 19 Markov decision processes 19 Confidence interval 18 Quantile regression 18 Clustering 17 Consistency 17 Dimension reduction 17 MCMC 16 Survival analysis 15 Functional data 14 Functional data analysis 14 Generalized linear models 14 Importance sampling 14 Longitudinal data 14 Maximum likelihood estimation 14 Nonparametric regression 14 Optimal control 14 Robust estimation 14 Core 13 Linear programming 13 Logistic regression 13 Monte Carlo simulation 13 Density estimation 12 Lasso 12 Optimization 12 Random effects 12 Regularization 12 Shapley value 12 Cluster analysis 11 Gibbs sampling 11
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Undetermined 6,248 Free 5
Type of publication
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Article 6,272 Book / Working Paper 17
Type of publication (narrower categories)
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Collection of articles of several authors 4 Sammelwerk 4 Aufsatzsammlung 2 Handbook 1 Handbuch 1
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Undetermined 6,277 English 12
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Balakrishnan, N. 40 Molenberghs, Geert 22 Tang, Man-Lai 22 Kundu, Debasis 21 Paula, Gilberto A. 16 Trenkler, Gotz 16 Lee, Sik-Yum 15 Cordeiro, Gauss M. 14 Hawkins, Douglas M. 14 Tijs, Stef 14 Tian, Guo-Liang 13 Cribari-Neto, Francisco 12 Nadarajah, Saralees 12 Tutz, Gerhard 12 Borm, Peter 11 Chen, Hubert J. 11 Hubert, Mia 11 Lee, Jae Won 11 Lemonte, Artur J. 11 Ortega, Edwin M.M. 11 Poon, Wai-Yin 11 Priebe, Carey E. 11 Rousseeuw, Peter J. 11 Bentler, Peter M. 10 Dodge, Yadolah 10 Hernández-Lerma, Onésimo 10 Agresti, Alan 9 Brown, Morton B. 9 Cavazos-Cadena, Rolando 9 Croux, Christophe 9 Gerlach, Richard 9 Lesaffre, Emmanuel 9 Liang, Hua 9 Lui, Kung-Jong 9 Shin, Dong Wan 9 Wang, Yong 9 D'Urso, Pierpaolo 8 Ferrari, Silvia L.P. 8 Fraiman, Ricardo 8 Gupta, Ramesh C. 8
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Computational Statistics & Data Analysis 4,738 Computational Statistics 1,534 Springer handbooks of computational statistics 3 Computational Statistics and Data Analysis 2 Computational Statistics and Data Analysis 143 (2020) 106843 1 Computational Statistics and Data Analysis 56 (2012) 1–14 1 Computational Statistics and Data Analysis, Forthcoming 1 Karabatsos, G. (2022). Approximate Bayesian computation using asymptotically normal point estimates. Computational Statistics, 1-38 1 Springer Handbooks of Computational Statistics 1 https://doi.org/10.1016/j.csda.2019.106843 Previous title "HOW MANY PARAMETERS DOES MY KERNEL DENSITY ESTIMATE HAVE?" 1
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RePEc 6,272 ECONIS (ZBW) 11 USB Cologne (EcoSocSci) 6
Showing 221 - 230 of 6,289
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Lower confidence limit for reliability based on grouped data using a quantile-filling algorithm
Zhang, Mimi; Hu, Qingpei; Xie, Min; Yu, Dan - In: Computational Statistics & Data Analysis 75 (2014) C, pp. 96-111
The aim of this paper is to propose an approach to constructing lower confidence limits for a reliability function and investigate the effect of a sampling scheme on the performance of the proposed approach. This is accomplished by using a data-completion algorithm and certain Monte Carlo...
Persistent link: https://www.econbiz.de/10010871428
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Maximum likelihood estimation of the Markov-switching GARCH model
Augustyniak, Maciej - In: Computational Statistics & Data Analysis 76 (2014) C, pp. 61-75
The Markov-switching GARCH model offers rich dynamics to model financial data. Estimating this path dependent model is a challenging task because exact computation of the likelihood is infeasible in practice. This difficulty led to estimation procedures either based on a simplification of the...
Persistent link: https://www.econbiz.de/10010871431
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A multivariate linear regression analysis using finite mixtures of t distributions
Galimberti, Giuliano; Soffritti, Gabriele - In: Computational Statistics & Data Analysis 71 (2014) C, pp. 138-150
Recently, finite mixture models have been used to model the distribution of the error terms in multivariate linear regression analysis. In particular, Gaussian mixture models have been employed. A novel approach that assumes that the error terms follow a finite mixture of t distributions is...
Persistent link: https://www.econbiz.de/10010871432
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Noisy kriging-based optimization methods: A unified implementation within the DiceOptim package
Picheny, Victor; Ginsbourger, David - In: Computational Statistics & Data Analysis 71 (2014) C, pp. 1035-1053
Kriging-based optimization relying on noisy evaluations of complex systems has recently motivated contributions from various research communities. Five strategies have been implemented in the DiceOptim package. The corresponding functions constitute a user-friendly tool for solving expensive...
Persistent link: https://www.econbiz.de/10010871436
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Transform both sides model: A parametric approach
Polpo, A.; de Campos, C.P.; Sinha, D.; Lipsitz, S.; Lin, J. - In: Computational Statistics & Data Analysis 71 (2014) C, pp. 903-913
A parametric regression model for right-censored data with a log-linear median regression function and a transformation in both response and regression parts, named parametric Transform-Both-Sides (TBS) model, is presented. The TBS model has a parameter that handles data asymmetry while allowing...
Persistent link: https://www.econbiz.de/10010871437
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Making classifier performance comparisons when ROC curves intersect
Gigliarano, Chiara; Figini, Silvia; Muliere, Pietro - In: Computational Statistics & Data Analysis 77 (2014) C, pp. 300-312
The ROC curve is one of the most common statistical tools useful to assess classifier performance. The selection of the best classifier when ROC curves intersect is quite challenging. A novel approach for model comparisons when ROC curves show intersections is proposed. In particular, the...
Persistent link: https://www.econbiz.de/10010871438
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Computation of maximum likelihood estimates for multiresponse generalized linear mixed models with non-nested, correlated random effects
Karl, Andrew T.; Yang, Yan; Lohr, Sharon L. - In: Computational Statistics & Data Analysis 73 (2014) C, pp. 146-162
Estimation of generalized linear mixed models (GLMMs) with non-nested random effects structures requires the approximation of high-dimensional integrals. Many existing methods are tailored to the low-dimensional integrals produced by nested designs. We explore the modifications that are required...
Persistent link: https://www.econbiz.de/10010871440
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Bootstrap corrections of treatment effect estimates following selection
Rosenkranz, Gerd K. - In: Computational Statistics & Data Analysis 69 (2014) C, pp. 220-227
Bias of treatment effect estimators can occur when the maximum effect of several treatments is to be determined or the effect of the selected treatment or subgroup has to be estimated. Since those estimates may contribute to the decision as to whether to continue a drug development program, to...
Persistent link: https://www.econbiz.de/10010871443
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A joint test for structural stability and a unit root in autoregressions
Pitarakis, Jean-Yves - In: Computational Statistics & Data Analysis 76 (2014) C, pp. 577-587
A test of the joint null hypothesis of parameter stability and a unit root within an ADF style autoregressive specification whose entire parameter structure is potentially subject to a structural break at an unknown time period is developed. The proposed test is a useful diagnostic tool for...
Persistent link: https://www.econbiz.de/10010871444
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Multivariate distributions with proportional reversed hazard marginals
Kundu, Debasis; Franco, Manuel; Vivo, Juana-Maria - In: Computational Statistics & Data Analysis 77 (2014) C, pp. 98-112
Several univariate proportional reversed hazard models have been proposed in the literature. Recently, Kundu and Gupta (2010) proposed a class of bivariate models with proportional reversed hazard marginals. It is observed that the proposed bivariate proportional reversed hazard models have a...
Persistent link: https://www.econbiz.de/10010871446
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