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Multivariate Analysis Commons

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Applied Statistics

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Articles 151 - 160 of 160

Full-Text Articles in Multivariate Analysis

Efficient Mean Estimation In Log-Normal Linear Models, Haipeng Shen, Zhengyuan Zhu Feb 2008

Efficient Mean Estimation In Log-Normal Linear Models, Haipeng Shen, Zhengyuan Zhu

Zhengyuan Zhu

Log-normal linear models are widely used in applications, and many times it is of interest to predict the response variable or to estimate the mean of the response variable at the original scale for a new set of covariate values. In this paper we consider the problem of efficient estimation of the conditional mean of the response variable at the original scale for log-normal linear models. Several existing estimators are reviewed first, including the maximum likelihood (ML) estimator, the restricted ML (REML) estimator, the uniformly minimum variance unbiased (UMVU) estimator, and a bias-corrected REML estimator. We then propose two estimators …


Economic Implications Of Copulas And Extremes, Lorán Chollete Jan 2008

Economic Implications Of Copulas And Extremes, Lorán Chollete

Lorán Chollete

No abstract provided.


The Risk Components Of Liquidity, Lorán Chollete, Randi Naes, Johannes Skjeltorp Dec 2007

The Risk Components Of Liquidity, Lorán Chollete, Randi Naes, Johannes Skjeltorp

Lorán Chollete

No abstract provided.


Additive Nonparametric Regression With Autocorrelated Errors, Michael S. Smith, C Wong, Robert Kohn Dec 1997

Additive Nonparametric Regression With Autocorrelated Errors, Michael S. Smith, C Wong, Robert Kohn

Michael Stanley Smith

A Bayesian approach is presented for nonparametric estimation of an additive regression model with autocorrelated errors. Each of the potentially nonlinear components is modelled as a regression spline using many knots, while the errors are modelled by a high order stationary autoregressive process parameterised in terms of its autocorrelations. The distribution of significant knots and partial autocorrelations is accounted for using subset selection. Our approach also allows the selection of a suitable transformation of the dependent variable. All aspects of the model are estimated simultaneously using Markov chain Monte Carlo. It is shown empirically that the proposed approach works well …


A Bayesian Approach To Bivariate Nonparametric Regression, Michael Smith, Robert Kohn Dec 1996

A Bayesian Approach To Bivariate Nonparametric Regression, Michael Smith, Robert Kohn

Michael Stanley Smith

No abstract provided.


Nonparametric Regression Using Bayesian Variable Selection, Michael Smith, Robert Kohn Dec 1995

Nonparametric Regression Using Bayesian Variable Selection, Michael Smith, Robert Kohn

Michael Stanley Smith

No abstract provided.


Finite Sample Performance Of Robust Bayesian Regression, Michael Smith, Sheather Simon, Kohn Robert Dec 1995

Finite Sample Performance Of Robust Bayesian Regression, Michael Smith, Sheather Simon, Kohn Robert

Michael Stanley Smith

No abstract provided.


Performance Indices For On-Ice Hockey Statistics, William (Bill) H. Williams Aug 1995

Performance Indices For On-Ice Hockey Statistics, William (Bill) H. Williams

Publications and Research

No abstract provided.


Book Review: Reasoning Agents In A Dynamic World: The Frame Problem. Kenneth M. Ford And Patrick J. Hayes, Eds.,, Jozsef A. Toth Jan 1995

Book Review: Reasoning Agents In A Dynamic World: The Frame Problem. Kenneth M. Ford And Patrick J. Hayes, Eds.,, Jozsef A. Toth

Jozsef A Toth Ph.D.

No abstract provided.


A Bayesian Approach To Additive Nonparametric Regression, Michael S. Smith, Robert Kohn Dec 1993

A Bayesian Approach To Additive Nonparametric Regression, Michael S. Smith, Robert Kohn

Michael Stanley Smith

This proceedings paper was the first to suggest using a Gaussian g-prior combined with a point mass to undertake Bayesian variable selection in a Gaussian linear regression model. It also was the first to suggest integrating out the regression parameters and variance in closed form, resulting in an efficient Gibbs sampling scheme. The idea was applied to estimate regression functions in an additive model by using a linear basis expansion for each component function in an additive model. The conference proceeding was eventually published in a slightly tighter form in Journal of Econometrics (1996).