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  • sbgcop: Semiparametric Bayesian Gaussian copula estimation This package estimates parameters of a G

    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

    標(biāo)簽: Semiparametric estimation parameters estimates

    上傳時(shí)間: 2014-12-08

    上傳用戶:一諾88

  • This is a simple algorithm that downloads trading data from yahoo database. It is basically a large

    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.

    標(biāo)簽: algorithm downloads basically database

    上傳時(shí)間: 2017-06-03

    上傳用戶:啊颯颯大師的

  • DLL 文件: mspdb60 或者 mspdb60.dll   DLL 名稱: Microsoft Program Database   描述:   mspdb60.dll是Microsof

    DLL 文件: mspdb60 或者 mspdb60.dll   DLL 名稱: Microsoft Program Database   描述:   mspdb60.dll是Microsoft Visual Studio編程數(shù)據(jù)庫支持相關(guān)文件。.   屬于: Visual Studio   系統(tǒng) DLL文件: 否   常見錯(cuò)誤: File Not Found, missing File, Exception Errors

    標(biāo)簽: mspdb DLL dll Microsoft

    上傳時(shí)間: 2014-11-29

    上傳用戶:gaome

  • The book consists of three sections. The first, foundations, provides a tutorial overview of the pri

    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.

    標(biāo)簽: foundations The consists sections

    上傳時(shí)間: 2017-06-22

    上傳用戶:lps11188

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