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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
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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 1,171 - 1,180 of 6,289
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Sparse principal components by semi-partition clustering
Enki, Doyo; Trendafilov, Nickolay - In: Computational Statistics 27 (2012) 4, pp. 605-626
A cluster-based method for constructing sparse principal components is proposed. The method initially forms clusters of variables, using a new clustering approach called the semi-partition, in two steps. First, the variables are ordered sequentially according to a criterion involving the...
Persistent link: https://www.econbiz.de/10010998526
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Estimating the system order by subspace methods
García-Hiernaux, Alfredo; Casals, José; Jerez, Miguel - In: Computational Statistics 27 (2012) 3, pp. 411-425
This paper discusses how to specify the order of a state-space model. To do so, we start by revising existing approaches and find in them two basic shortcomings: (i) some of them have a poor performance in short samples and (ii) most of them are not robust, meaning that their performance...
Persistent link: https://www.econbiz.de/10010998528
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Inference for Cox’s regression models via adjusted empirical likelihood
Zhao, Yichuan; Jinnah, Ali - In: Computational Statistics 27 (2012) 1, pp. 1-12
Persistent link: https://www.econbiz.de/10010539373
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Nonparametric estimation in [alpha]-series processes
Aydogdu, Halil; Kara, Mahmut - In: Computational Statistics & Data Analysis 56 (2012) 1, pp. 190-201
A counting process with the interoccurrence times X1,X2,... is an [alpha]-series process if there exists a real number [alpha] such that (k[alpha]Xk)k=1,2,... forms a renewal process. The nonparametric inference problem in an [alpha]-series process is taken into consideration. The Mann test is...
Persistent link: https://www.econbiz.de/10009274844
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Inference about clustering and parametric assumptions in covariance matrix estimation
Packalen, Mikko; Wirjanto, Tony S. - In: Computational Statistics & Data Analysis 56 (2012) 1, pp. 1-14
Selecting an estimator for the covariance matrix of a regression's parameter estimates is an important step in hypothesis testing. From less to more robust estimators, the choices available to researchers include Eicker/White heteroskedasticity-robust estimator, cluster-robust estimator, and...
Persistent link: https://www.econbiz.de/10009274857
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Bayesian estimation of generalized hyperbolic skewed student GARCH models
Deschamps, Philippe J. - In: Computational Statistics & Data Analysis 56 (2012) 11, pp. 3035-3054
Efficient posterior simulators for two GARCH models with generalized hyperbolic disturbances are presented. The first model, GHt-GARCH, is a threshold GARCH with a skewed and heavy-tailed error distribution; in this model, the latent variables that account for skewness and heavy tails are...
Persistent link: https://www.econbiz.de/10010617630
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Covariate unit root tests with good size and power
Fossati, Sebastian - In: Computational Statistics & Data Analysis 56 (2012) 11, pp. 3070-3079
The selection of the truncation lag for covariate unit root tests is analyzed using Monte Carlo simulation. It is shown that standard information criteria such as the BIC or the AIC select lag orders that are too small and can result in tests with large size distortions. Modified information...
Persistent link: https://www.econbiz.de/10010617631
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A Bayesian conditional autoregressive geometric process model for range data
Chan, J.S.K.; Lam, C.P.Y.; Yu, P.L.H.; Choy, S.T.B.; … - In: Computational Statistics & Data Analysis 56 (2012) 11, pp. 3006-3019
Extreme value theories indicate that the range is an efficient estimator of local volatility in financial time series. A geometric process (GP) framework that incorporates the conditional autoregressive range (CARR)-type mean function is presented for range data. The proposed model, called the...
Persistent link: https://www.econbiz.de/10010617632
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Simple VARs cannot approximate Markov switching asset allocation decisions: An out-of-sample assessment
Guidolin, Massimo; Hyde, Stuart - In: Computational Statistics & Data Analysis 56 (2012) 11, pp. 3546-3566
In a typical strategic asset allocation problem, the out-of-sample certainty equivalent returns for a long-horizon investor with constant relative risk aversion computed from a range of vector autoregressions (VARs) are compared with those from nonlinear models that account for bull and bear...
Persistent link: https://www.econbiz.de/10010617633
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Modeling dynamic effects of promotion on interpurchase times
Fok, Dennis; Paap, Richard; Franses, Philip Hans - In: Computational Statistics & Data Analysis 56 (2012) 11, pp. 3055-3069
Dynamic effects of marketing-mix variables on interpurchase times can be analyzed in the context of a duration model. Specifically, this can be done by extending the accelerated failure-time model with an autoregressive structure. An important feature of the model is that it allows for different...
Persistent link: https://www.econbiz.de/10010617634
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