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Computation of Multivariate Normal and t Probabilities [electronic resource] /by Alan Genz, Frank Bretz.

by Genz, Alan [author.]; Bretz, Frank [author.]; SpringerLink (Online service).
Material type: materialTypeLabelBookSeries: Lecture Notes in Statistics: 195Publisher: Berlin, Heidelberg : Springer Berlin Heidelberg, 2009.Description: online resource.ISBN: 9783642016899.Subject(s): Statistics | Mathematical statistics | Statistics | Statistics and Computing/Statistics Programs | Statistical Theory and MethodsDDC classification: 519.5 Online resources: Click here to access online
Contents:
Introduction -- Special Cases -- Methods That Approximate the Problem -- Methods That Approximate the Integral -- Further Topics: Linear Inequality Constraints -- Singular Distributions -- Singular Distributions -- Numerical Tests -- Software Implementations -- Applications -- Description of the R Functions -- Description of the MATLAB Functions.
In: Springer eBooksSummary: Multivariate normal and t probabilities are needed for statistical inference in many applications. Modern statistical computation packages provide functions for the computation of these probabilities for problems with one or two variables. This book describes recently developed methods for accurate and efficient computation of the required probability values for problems with two or more variables. The book discusses methods for specialized problems as well as methods for general problems. The book includes examples that illustrate the probability computations for a variety of applications.
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Introduction -- Special Cases -- Methods That Approximate the Problem -- Methods That Approximate the Integral -- Further Topics: Linear Inequality Constraints -- Singular Distributions -- Singular Distributions -- Numerical Tests -- Software Implementations -- Applications -- Description of the R Functions -- Description of the MATLAB Functions.

Multivariate normal and t probabilities are needed for statistical inference in many applications. Modern statistical computation packages provide functions for the computation of these probabilities for problems with one or two variables. This book describes recently developed methods for accurate and efficient computation of the required probability values for problems with two or more variables. The book discusses methods for specialized problems as well as methods for general problems. The book includes examples that illustrate the probability computations for a variety of applications.

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