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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 901 - 910 of 6,289
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Parameter estimation for the discretely observed fractional Ornstein–Uhlenbeck process and the Yuima R package
Brouste, Alexandre; Iacus, Stefano - In: Computational Statistics 28 (2013) 4, pp. 1529-1547
This paper proposes consistent and asymptotically Gaussian estimators for the parameters <InlineEquation ID="IEq1"> <EquationSource Format="TEX">$$\lambda , \sigma $$</EquationSource> </InlineEquation> and <InlineEquation ID="IEq2"> <EquationSource Format="TEX">$$H$$</EquationSource> </InlineEquation> of the discretely observed fractional Ornstein–Uhlenbeck process solution of the stochastic differential equation <InlineEquation ID="IEq3"> <EquationSource Format="TEX">$$d Y_t=-\lambda Y_t dt + \sigma d W_t^H$$</EquationSource> </InlineEquation>, where <InlineEquation ID="IEq4"> <EquationSource...</equationsource></inlineequation></equationsource></inlineequation></equationsource></inlineequation></equationsource></inlineequation>
Persistent link: https://www.econbiz.de/10010998485
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MCMC using Markov bases for computing <InlineEquation ID="IEq1"> <EquationSource Format="TEX">$$p$$</EquationSource> </InlineEquation>-values in decomposable log-linear models
Kuroda, Masahiro; Hashiguchi, Hiroki; Nakagawa, Shigekazu; … - In: Computational Statistics 28 (2013) 2, pp. 831-850
We derive an explicit form of a Markov basis on the junction tree for a decomposable log-linear model. Then we give a description of a Markov basis characterized by global Markov properties associated with the graph of a decomposable log-linear model and show how to use the Markov basis for...
Persistent link: https://www.econbiz.de/10010998491
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An efficient method for constructing uniform designs with large size
Zhou, Yong-Dao; Fang, Kai-Tai - In: Computational Statistics 28 (2013) 3, pp. 1319-1331
Many construction methods for (nearly) uniform designs have been proposed under the centered <InlineEquation ID="IEq1"> <EquationSource Format="TEX">$$L_2$$</EquationSource> </InlineEquation>-discrepancy, and most of them are only suitable for constructing designs with small size. This paper proposes a new method, called mixture method (MM), to construct nearly...</equationsource></inlineequation>
Persistent link: https://www.econbiz.de/10010998517
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Handbook of computational finance
Duan, Jin-Chuan (contributor) - 2012
Persistent link: https://www.econbiz.de/10009499313
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Stochastic differential portfolio games for an insurer in a jump-diffusion risk process
Lin, Xiang; Zhang, Chunhong; Siu, Tak - In: Computational Statistics 75 (2012) 1, pp. 83-100
We discuss an optimal portfolio selection problem of an insurer who faces model uncertainty in a jump-diffusion risk model using a game theoretic approach. In particular, the optimal portfolio selection problem is formulated as a two-person, zero-sum, stochastic differential game between the...
Persistent link: https://www.econbiz.de/10010759576
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A private contributions game for joint replenishment
Körpeoğlu, Evren; Şen, Alper; Güler, Kemal - In: Computational Statistics 75 (2012) 1, pp. 67-82
We study a non-cooperative game for joint replenishment by n firms that operate under an EOQ-like setting. Each firm decides whether to replenish independently or to participate in joint replenishment, and how much to contribute to joint ordering costs in case of participation. Joint...
Persistent link: https://www.econbiz.de/10010759595
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A new second-order corrector interior-point algorithm for semidefinite programming
Liu, Changhe; Liu, Hongwei - In: Computational Statistics 75 (2012) 2, pp. 165-183
In this paper, we propose a second-order corrector interior-point algorithm for semidefinite programming (SDP). This algorithm is based on the wide neighborhood. The complexity bound is $${O(\sqrt{n}L)}$$ for the Nesterov-Todd direction, which coincides with the best known complexity results for...
Persistent link: https://www.econbiz.de/10010759596
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The multiple-partners assignment game with heterogeneous sales and multi-unit demands: competitive equilibria
Jaume, Daniel; Massó, Jordi; Neme, Alejandro - In: Computational Statistics 76 (2012) 2, pp. 161-187
A multiple-partners assignment game with heterogeneous sales and multi-unit demands consists of a set of sellers that own a given number of indivisible units of potentially many different goods and a set of buyers who value those units and want to buy at most an exogenously fixed number of...
Persistent link: https://www.econbiz.de/10010759599
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The power of weather
Huurman, Christian; Ravazzolo, Francesco; Zhou, Chen - In: Computational Statistics & Data Analysis 56 (2012) 11, pp. 3793-3807
Weather information demonstrates predictive power in forecasting electricity prices in day-ahead markets in real time. In particular, next-day weather forecasts improve the forecast accuracy of Scandinavian day-ahead electricity prices in terms of point and density forecasts. This suggests that...
Persistent link: https://www.econbiz.de/10010871303
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The robust estimation method for a finite mixture of Poisson mixed-effect models
Xiang, Liming; Yau, Kelvin K.W.; Lee, Andy H. - In: Computational Statistics & Data Analysis 56 (2012) 6, pp. 1994-2005
When analyzing clustered count data derived from several latent subpopulations, the finite mixture of the Poisson mixed-effect model is an immediate strategy to model the underlying heterogeneity. Within the generalized linear mixed model framework, parameters in such a model are often estimated...
Persistent link: https://www.econbiz.de/10010871311
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