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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 1,101 - 1,110 of 6,289
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A particle swarm algorithm with broad applicability in shape-constrained estimation
Wolters, Mark A. - In: Computational Statistics & Data Analysis 56 (2012) 10, pp. 2965-2975
In nonparametric function estimation, the inclusion of shape constraints can confer several advantages, including improved estimation accuracy, reduced sensitivity to smoothing parameters, and control over the qualitative appearance of the estimate. Finding shape-restricted estimates may require...
Persistent link: https://www.econbiz.de/10010574484
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Multivariate probit analysis of binary familial data using stochastic representations
Deng, Yihao; Sabo, Roy T.; Chaganty, N. Rao - In: Computational Statistics & Data Analysis 56 (2012) 3, pp. 656-663
The probit function is an alternative transformation to the logistic function in the analysis of binary data. However, use of the probit function is prohibitively complicated for cases of multivariate or repeated-measure binary responses, as integrations involving the multivariate normal...
Persistent link: https://www.econbiz.de/10010574485
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Computation of optimal plotting points based on Pitman closeness with an application to goodness-of-fit for location-scale families
Balakrishnan, N.; Davies, K.F.; Keating, J.P.; Mason, R.L. - In: Computational Statistics & Data Analysis 56 (2012) 9, pp. 2637-2649
Plotting points of order statistics are often used in the determination of goodness-of-fit of observed data to theoretical percentiles. Plotting points are usually determined by using nonparametric methods which produce, for example, the mean- and median-ranks. Here, we use a distribution-based...
Persistent link: https://www.econbiz.de/10010574486
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Credit scoring analysis using a fuzzy probabilistic rough set model
Capotorti, Andrea; Barbanera, Eva - In: Computational Statistics & Data Analysis 56 (2012) 4, pp. 981-994
Credit scoring analysis is an important activity, especially nowadays after a huge number of defaults has been one of the main causes of the financial crisis. Among the many different tools used to model credit risk, the recent development of rough set models has proved effective. The original...
Persistent link: https://www.econbiz.de/10010574487
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Asymptotically efficient estimation of the conditional expected shortfall
Leorato, Samantha; Peracchi, Franco; Tanase, Andrei V. - In: Computational Statistics & Data Analysis 56 (2012) 4, pp. 768-784
A procedure for efficient estimation of the trimmed mean of a random variable conditional on a set of covariates is proposed. For concreteness, the focus is on a financial application where the trimmed mean of interest corresponds to the conditional expected shortfall, which is known to be a...
Persistent link: https://www.econbiz.de/10010574488
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Root selection in normal mixture models
Seo, Byungtae; Kim, Daeyoung - In: Computational Statistics & Data Analysis 56 (2012) 8, pp. 2454-2470
Finite mixtures of normal distributions are attractive in identifying the underlying group structure in the data. However, it is a challenging task to do statistical inference in normal mixture models using the method of maximum likelihood, due to the unbounded likelihood and the existence of...
Persistent link: https://www.econbiz.de/10010574489
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Large gap imputation in remote sensed imagery of the environment
Rulloni, Valeria; Bustos, Oscar; Flesia, Ana Georgina - In: Computational Statistics & Data Analysis 56 (2012) 8, pp. 2388-2403
Imputation of missing data in large regions of satellite imagery is necessary when the acquired image has been damaged by shadows due to clouds, or information gaps produced by sensor failure.
Persistent link: https://www.econbiz.de/10010574490
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Sequential methods for pharmacogenetic studies
Todd, Susan; Fazil Baksh, M.; Whitehead, John - In: Computational Statistics & Data Analysis 56 (2012) 5, pp. 1221-1231
A study or experiment can be described as sequential if its design includes one or more interim analyses at which it is possible to stop the study, having reached a definitive conclusion concerning the primary question of interest. The potential of the sequential study to terminate earlier than...
Persistent link: https://www.econbiz.de/10010574491
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Nonparametric regression models for right-censored data using Bernstein polynomials
Osman, Muhtarjan; Ghosh, Sujit K. - In: Computational Statistics & Data Analysis 56 (2012) 3, pp. 559-573
In some applications of survival analysis with covariates, the commonly used semiparametric assumptions (e.g., proportional hazards) may turn out to be stringent and unrealistic, particularly when there is scientific background to believe that survival curves under different covariate...
Persistent link: https://www.econbiz.de/10010574492
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Comparison of quantiles for several normal populations
Li, Xinmin; Tian, Lili; Wang, Juan; Muindi, Josephia R. - In: Computational Statistics & Data Analysis 56 (2012) 6, pp. 2129-2138
For the purpose of comparison between several independent populations, many procedures exist for testing equality of means or medians among the groups. However, the mean or the median do not determine the entire distribution. This paper addresses the problem of testing the equality of quantiles...
Persistent link: https://www.econbiz.de/10010574493
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