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
标签： Semiparametric estimation parameters estimates
上传时间： 2014-12-08
上传用户：一诺88
This is a simple algorithm that downloads trading data from yahoo database. It is basically a large scale application of sqq.m which was originally submitted by Michael Boldin, link at acknowledgements. Some of the functionalities of the package: - User defined ticker list. - Function for downloading most recent SP500 composition in ticker list. - Control for bad data (e.g. a certain percentage of prices Missing). - Choice of frequency of data (e.g. weekly prices). - Choice of starting and ending data. - Function for saving the whole data in a pre-formatted excel file together with a full reports on Missing data.
标签： algorithm downloads basically database
上传时间： 2017-06-03
上传用户：啊飒飒大师的
DLL 文件： mspdb60 或者 mspdb60.dll DLL 名称： Microsoft Program Database 描述： mspdb60.dll是Microsoft Visual Studio编程数据库支持相关文件。. 属于： Visual Studio 系统 DLL文件： 否 常见错误： File Not Found, Missing File, Exception Errors
上传时间： 2014-11-29
上传用户：gaome
The book consists of three sections. The first, foundations, provides a tutorial overview of the principles underlying data mining algorithms and their application. The presentation emphasizes intuition rather than rigor. The second section, data mining algorithms, shows how algorithms are constructed to solve specific problems in a principled manner. The algorithms covered include trees and rules for classification and regression, association rules, belief networks, classical statistical models, nonlinear models such as neural networks, and local memory-based models. The third section shows how all of the preceding analysis fits together when applied to real-world data mining problems. Topics include the role of metadata, how to handle Missing data, and data preprocessing.
标签： foundations The consists sections
上传时间： 2017-06-22
上传用户：lps11188
