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[Other resource] sbgcop_0.95.tar DL : 0: sbgcop: Semiparametric Bayesian Gaussian copula estimation This package estimates parameters of a Gaussian copula, treating the univariate marginal distributions as nuisance parameters as described in Hoff(2007). It also provides a semiparametric imputation procedure for missing multivariate data. Version: 0.95 Date: 2007-03-09 Author: Peter Hoff Maintainer: Peter Hoff <hoff at stat.washington.edu> License: GPL Version 2 or later URL: http://www.stat.washington.edu/hoff CRAN checks: sbgcop results Downloads: Package source: sbgcop_0.95.tar.gz MacOS X binary: sbgcop_0.95.tgz Windows binary: sbgcop_0.95.zip Reference manual: sbgcop.pdf
Date : 2008-10-13 Size : 5.15kb User : 陈远
[Other resource] sbgcop_0.95 DL : 0: sbgcop: Semiparametric Bayesian Gaussian copula estimation This package estimates parameters of a Gaussian copula, treating the univariate marginal distributions as nuisance parameters as described in Hoff(2007). It also provides a semiparametric imputation procedure for missing multivariate data. Version: 0.95 Date: 2007-03-09 Author: Peter Hoff Maintainer: Peter Hoff <hoff at stat.washington.edu> License: GPL Version 2 or later URL: http://www.stat.washington.edu/hoff CRAN checks: sbgcop results Downloads: Windows binary: sbgcop_0.95.zip
Date : 2008-10-13 Size : 39.8kb User : 陈远
[Other resource] sbgcop DL : 0: sbgcop: Semiparametric Bayesian Gaussian copula estimation This package estimates parameters of a Gaussian copula, treating the univariate marginal distributions as nuisance parameters as described in Hoff(2007). It also provides a semiparametric imputation procedure for missing multivariate data. Version: 0.95 Date: 2007-03-09 Author: Peter Hoff Maintainer: Peter Hoff <hoff at stat.washington.edu> License: GPL Version 2 or later URL: http://www.stat.washington.edu/hoff CRAN checks: sbgcop results Downloads: Reference manual: sbgcop.pdf
Date : 2008-10-13 Size : 92kb User : 陈远
[Other resource] imputation.tar DL : 0: 一个很有用的EM算法程序包，可用于混合高斯模型，值得一看哦
Date : 2008-10-13 Size : 17.71kb User : 林枫
[matlab] sbgcop_0.95.tar DL : 0: sbgcop: Semiparametric Bayesian Gaussian copula estimation This package estimates parameters of a Gaussian copula, treating the univariate marginal distributions as nuisance parameters as described in Hoff(2007). It also provides a semiparametric imputation procedure for missing multivariate data. Version: 0.95 Date: 2007-03-09 Author: Peter Hoff Maintainer: Peter Hoff <hoff at stat.washington.edu> License: GPL Version 2 or later URL: http://www.stat.washington.edu/hoff CRAN checks: sbgcop results Downloads: Package source: sbgcop_0.95.tar.gz MacOS X binary: sbgcop_0.95.tgz Windows binary: sbgcop_0.95.zip Reference manual: sbgcop.pdf
Date : 2025-07-16 Size : 5kb User : cy
[matlab] sbgcop_0.95 DL : 0: sbgcop: Semiparametric Bayesian Gaussian copula estimation This package estimates parameters of a Gaussian copula, treating the univariate marginal distributions as nuisance parameters as described in Hoff(2007). It also provides a semiparametric imputation procedure for missing multivariate data. Version: 0.95 Date: 2007-03-09 Author: Peter Hoff Maintainer: Peter Hoff <hoff at stat.washington.edu> License: GPL Version 2 or later URL: http://www.stat.washington.edu/hoff CRAN checks: sbgcop results Downloads: Windows binary: sbgcop_0.95.zip
Date : 2025-07-16 Size : 39kb User : cy
[Other] sbgcop DL : 0: sbgcop: Semiparametric Bayesian Gaussian copula estimation This package estimates parameters of a Gaussian copula, treating the univariate marginal distributions as nuisance parameters as described in Hoff(2007). It also provides a semiparametric imputation procedure for missing multivariate data. Version: 0.95 Date: 2007-03-09 Author: Peter Hoff Maintainer: Peter Hoff <hoff at stat.washington.edu> License: GPL Version 2 or later URL: http://www.stat.washington.edu/hoff CRAN checks: sbgcop results Downloads: Reference manual: sbgcop.pdf
Date : 2025-07-16 Size : 92kb User : cy
[AI-NN-PR] imputation.tar DL : 0: 一个很有用的EM算法程序包，可用于混合高斯模型，值得一看哦-A useful package of the EM algorithm can be used in mixed-Gaussian model, see Oh
Date : 2025-07-16 Size : 18kb User : 林枫
[matlab] imputation DL : 0: 不完全数据分析MATLAB程序（部分信息重建）：最小均方估计、协方差矩阵、缺失值推测-Analysis of incomplete datasets: Estimation of mean values and covariance matrices and imputation of missing values
Date : 2025-07-16 Size : 24kb User : 邓玥琳
[Other] phase.2.1.1.source.tar DL : 0: 预测单倍型根据基因型数据,根据已经知道的数据,预测未知的数据- The program PHASE implements methods for estimating haplotypes from population genotype data described in Stephens, M., and Donnelly, P. (2003). A comparison of Bayesian methods for haplotype reconstruction from population genotype data. American Journal of Human Genetics, 73:1162-1169. Stephens, M., Smith, N., and Donnelly, P. (2001). A new statistical method for haplotype reconstruction from population data. American Journal of Human Genetics, 68, 978--989. Stephens, M., and Scheet, P. (2005). Accounting for Decay of Linkage Disequilibrium in Haplotype Inference and Missing-Data Imputation. American Journal of Human Genetics, 76:449-462. The software also incorporates methods for estimating recombination rates, and identifying recombination hotspots: Crawford et al (2004). Evidence for substantial fine-scale variation in recombination rates across the human genome. Nature Genetics,. The software is free for non-commercial use, and may be licensed for commercial use
Date : 2025-07-16 Size : 241kb User : zcrself
[Other] rotational_sampling DL : 0: paper describing successive imputation
Date : 2025-07-16 Size : 1.72mb User : Bimlesh
[matlab] hgfrz DL : 0: This package estimates parameters of a Gaussian copula, treating the univariate marginal distributions as nuisance parameters as described in Hoff(2007). It also provides a semiparametric imputation procedure for missing multivariate data.
Date : 2025-07-16 Size : 2kb User : FOUFOU2
[Program doc] C2 DL : 0: The Impact of Missing Values Imputation Methods in cDNA Microarrays on Downstream Data Analysis
Date : 2025-07-16 Size : 318kb User : Mohammed Adel
[matlab] sun1 DL : 0: Imputation Report – HapMap 3 reference panel
Date : 2025-07-16 Size : 2.44mb User : sundari
[matlab] ImputationSOM DL : 0: SOM toolbox used as an imputation method
Date : 2025-07-16 Size : 3kb User : hell4all
[Windows Develop] inpaint_nans DL : 0: 缺失数据的填补函数，用来估计缺失的数据。 single and mutiple imputation-Solves approximation to one of several pdes to interpolate and extrapolate holes in an array
Date : 2025-07-16 Size : 4kb User : 王进
[matlab] imputation DL : 0: Matlab 工具箱，基于正则期望最大化方法(Regularied Expectation Maximization)的数据填充。-A Matlab toolbox based on the regularied expectation maximization (RegEM) based data imputation.
Date : 2025-07-16 Size : 28kb User : zxcvb
[Algorithm] MI_Exp DL : 0: 平均插补：对缺失的数据，用已记录数据的平均值进行填补-mean imputation
Date : 2025-07-16 Size : 2kb User : fan
[SCM] chabu DL : 0: C语言实现的插补程序，用于数控开发，方便移植51或tsm32-C language implementation of the imputation procedure for numerical development, ease of porting 51 or tsm32
Date : 2025-07-16 Size : 1kb User : yelang
[Compress-Decompress algrithms] tMiefore DL : 0: tMissing data in large insurance datasets affects the learning and classification accuracies in predictivemodelling. Insurance datasets will continue to increase in size as more variables are added to aid inmanaging client risk and will therefore be even more vulnerable to missing data. This paper proposes ahybrid multi-layered artificial immune system and genetic algorithm for partial imputation of missingdata in datasets with numerous variables. The multi-layered artificial immune system creates and storesantibodies that bind to and annihilate an antigen. The genetic algorithm optimises the learning processof a stimulated antibody. The uation of the imputation is performed using the RIPPER, k-nearestneighbour-tMissing data in large insurance datasets affects the learning and classification accuracies in predictivemodelling. Insurance datasets will continue to increase in size as more variables are added to aid inmanaging client risk and will therefore be even more vulnerable to missing data. This paper proposes ahybrid multi-layered artificial immune system and genetic algorithm for partial imputation of missingdata in datasets with numerous variables. The multi-layered artificial immune system creates and storesantibodies that bind to and annihilate an antigen. The genetic algorithm optimises the learning processof a stimulated antibody. The uation of the imputation is performed using the RIPPER, k-nearestneighbour
Date : 2025-07-16 Size : 1.86mb User : yangs

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