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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 1,581 - 1,590 of 6,289
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Full and conditional likelihood approaches for hazard change-point estimation with truncated and censored data
Gürler, Ülkü; Deniz Yenigün, C. - In: Computational Statistics & Data Analysis 55 (2011) 10, pp. 2856-2870
Hazard function plays an important role in reliability and survival analysis. In some real life applications, abrupt changes in the hazard function may be observed and it is of interest to detect the location and the size of the change. Hazard models with a change-point are considered when the...
Persistent link: https://www.econbiz.de/10009142750
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Power and sample size requirements for non-inferiority in studies comparing two matched proportions where the events are correlated
Nam, Jun-mo - In: Computational Statistics & Data Analysis 55 (2011) 10, pp. 2880-2887
Consider clustered matched-pair studies for non-inferiority where clusters are independent but units in a cluster are correlated. An inexpensive new procedure and the expensive standard one are applied to each unit and outcomes are binary responses. Appropriate statistics testing non-inferiority...
Persistent link: https://www.econbiz.de/10009142751
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Adaptive EWMA procedures for monitoring processes subject to linear drifts
Su, Yan; Shu, Lianjie; Tsui, Kwok-Leung - In: Computational Statistics & Data Analysis 55 (2011) 10, pp. 2819-2829
The conventional Statistical Process Control (SPC) techniques have been focused mostly on the detection of step changes in process means. However, there are often settings for monitoring linear drifts in process means, e.g., the gradual change due to tool wear or similar causes. The adaptive...
Persistent link: https://www.econbiz.de/10009142752
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CUSUM control charts for monitoring optimal portfolio weights
Golosnoy, Vasyl; Ragulin, Sergiy; Schmid, Wolfgang - In: Computational Statistics & Data Analysis 55 (2011) 11, pp. 2991-3009
A portfolio investor requires statistical tools for the timely detection of changes in the optimal portfolio composition. Several multivariate cumulative sum (CUSUM) control charts are proposed for the purpose of monitoring optimal portfolio weights. The ability of the CUSUM schemes to detect...
Persistent link: https://www.econbiz.de/10009142753
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On the Fernández-Steel distribution: Inference and application
Castillo, Nabor O.; Gómez, Héctor W.; Leiva, Víctor; … - In: Computational Statistics & Data Analysis 55 (2011) 11, pp. 2951-2961
In this article, we perform statistical inference on a skew model that belongs to a class of distributions proposed by Fernández and Steel (1998). Specifically, we introduce two ways to represent this model by means of which moments and generation of random numbers can be obtained. In addition,...
Persistent link: https://www.econbiz.de/10009142754
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Scale space multiresolution analysis of random signals
Holmström, Lasse; Pasanen, Leena; Furrer, Reinhard; … - In: Computational Statistics & Data Analysis 55 (2011) 10, pp. 2840-2855
A method to capture the scale-dependent features in a random signal is proposed with the main focus on images and spatial fields defined on a regular grid. A technique based on scale space smoothing is used. However, while the usual scale space analysis approach is to suppress detail by...
Persistent link: https://www.econbiz.de/10009142755
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Objective Bayesian analysis of accelerated competing failure models under Type-I censoring
Xu, Ancha; Tang, Yincai - In: Computational Statistics & Data Analysis 55 (2011) 10, pp. 2830-2839
This paper discusses the Bayesian inference of accelerated life tests (ALT) in the presence of competing failure causes. The time to failure due to a specific cause is described by a Weibull distribution. A two-stage approach is utilized to obtain the estimates of parameters in the model. We use...
Persistent link: https://www.econbiz.de/10009142756
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Information methods for model selection in linear mixed effects models with application to HCV data
Dimova, Rositsa B.; Markatou, Marianthi; Talal, Andrew H. - In: Computational Statistics & Data Analysis 55 (2011) 9, pp. 2677-2697
In this paper, we derive a small sample Akaike information criterion, based on the maximized loglikelihood, and a small sample information criterion based on the maximized restricted loglikelihood in the linear mixed effects model when the covariance matrix of the random effects is known. Small...
Persistent link: https://www.econbiz.de/10009142757
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Comparative assessment of trial-level surrogacy measures for candidate time-to-event surrogate endpoints in clinical trials
Shi, Qian; Renfro, Lindsay A.; Bot, Brian M.; … - In: Computational Statistics & Data Analysis 55 (2011) 9, pp. 2748-2757
Various meta-analytical approaches have been applied to evaluate putative surrogate endpoints (S) of primary clinical endpoints (T), however a systematic assessment of their performance is lacking. Existing methods in the meta-analytic framework can be grouped into two types--conventional and...
Persistent link: https://www.econbiz.de/10009142758
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Model-based SIR for dimension reduction
Scrucca, Luca - In: Computational Statistics & Data Analysis 55 (2011) 11, pp. 3010-3026
A new dimension reduction method based on Gaussian finite mixtures is proposed as an extension to sliced inverse regression (SIR). The model-based SIR (MSIR)1 approach allows the main limitation of SIR to be overcome, i.e., failure in the presence of regression symmetric relationships, without...
Persistent link: https://www.econbiz.de/10009142759
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