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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
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Article 6,272 Book / Working Paper 17
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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 1,701 - 1,710 of 6,289
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An acceleration method for Ten Berge et al.’s algorithm for orthogonal INDSCAL
Takane, Yoshio; Jung, Kwanghee; Hwang, Heungsun - In: Computational Statistics 25 (2010) 3, pp. 409-428
Persistent link: https://www.econbiz.de/10008456128
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A note on the prior parameter choice in finite mixture models of distributions from exponential families
Rufo, M.; Martín, J.; Pérez, C. - In: Computational Statistics 25 (2010) 3, pp. 537-550
Persistent link: https://www.econbiz.de/10008456129
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Confidence intervals for the regression parameter based on weighted log-rank estimating functions
Lee, Seung-Hwan - In: Computational Statistics 25 (2010) 3, pp. 429-440
Persistent link: https://www.econbiz.de/10008456130
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Handbook of partial least squares : concepts, methods and applications
Esposito Vinzi, Vincenzo (contributor) - 2010
Persistent link: https://www.econbiz.de/10014013078
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Handbook of partial least squares : concepts, methods and applications
Esposito Vinzi, Vincenzo (contributor) - 2010
Persistent link: https://www.econbiz.de/10003314107
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Mixtures of GAMs for habitat suitability analysis with overdispersed presence/absence data
Pleydell, David R.J.; Chrétien, Stéphane - In: Computational Statistics & Data Analysis 54 (2010) 5, pp. 1405-1418
A new approach to species distribution modelling based on unsupervised classification via a finite mixture of GAMs incorporating habitat suitability curves is proposed. A tailored EM algorithm is outlined for computing maximum likelihood estimates. Several submodels incorporating various...
Persistent link: https://www.econbiz.de/10008550806
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Finite-sample investigation of likelihood and Bayes inference for the symmetric von Mises-Fisher distribution
Bingham, Melissa A.; Nordman, Daniel J.; Vardeman, … - In: Computational Statistics & Data Analysis 54 (2010) 5, pp. 1317-1327
We consider likelihood and Bayes analyses for the symmetric matrix von Mises-Fisher (matrix Fisher) distribution, which is a common model for three-dimensional orientations (represented by 3x3 orthogonal matrices with a positive determinant). One important characteristic of this model is a 3x3...
Persistent link: https://www.econbiz.de/10008550807
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Simplified modeling strategies for surrogate validation with multivariate failure-time data
Cortiñas Abrahantes, José; Burzykowski, Tomasz - In: Computational Statistics & Data Analysis 54 (2010) 6, pp. 1457-1466
The linear mixed effects model has become a standard tool for the analysis of continuous hierarchical data such as, for example, repeated measures or data from meta-analyses. However, in certain situations the model does pose unavoidable computational problems. In the context of surrogate...
Persistent link: https://www.econbiz.de/10008550808
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Influence analyses of skew-normal/independent linear mixed models
Zeller, Camila B.; Labra, Filidor V.; Lachos, Victor H.; … - In: Computational Statistics & Data Analysis 54 (2010) 5, pp. 1266-1280
A extension of some diagnostic procedures to skew-normal/independent linear mixed models is discussed. This class provides a useful generalization of normal (and skew-normal) linear mixed models since it is assumed that the random effects and the random error terms follow jointly a multivariate...
Persistent link: https://www.econbiz.de/10008550809
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Binary trees for dissimilarity data
Piccarreta, Raffaella - In: Computational Statistics & Data Analysis 54 (2010) 6, pp. 1516-1524
Binary segmentation procedures (in particular, classification and regression trees) are extended to study the relation between dissimilarity data and a set of explanatory variables. The proposed split criterion is very flexible, and can be applied to a wide range of data (e.g., mixed types of...
Persistent link: https://www.econbiz.de/10008550810
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