Statistical Models Commons^™

Addition To Pglr Chap 6, Joseph M. Hilbe

Joseph M Hilbe

Addition to Chapter 6 in Practical Guide to Logistic Regression. Added section on Bayesian logistic regression using Stata.

Negative Binomial Regerssion, 2nd Ed, 2nd Print, Errata And Comments, Joseph Hilbe

Joseph M Hilbe

Errata and Comments for 2nd printing of NBR2, 2nd edition. Previous errata from first printing all corrected. Some added and new text as well.

Errata - Logistic Regression Models, Joseph Hilbe

Joseph M Hilbe

Errata for Logistic Regression Models, 4th Printing

Interpretation And Prediction Of A Logistic Model, Joseph M. Hilbe

Joseph M Hilbe

A basic overview of how to model and interpret a logistic regression model, as well as how to obtain the predicted probability or fit of the model and calculate its confidence intervals. R code used for all examples; some Stata is provided as a contrast.

Beta Binomial Regression, Joseph M. Hilbe

Joseph M Hilbe

Monograph on how to construct, interpret and evaluate beta, beta binomial, and zero inflated beta-binomial regression models. Stata and R code used for examples.

Nbr2 Errata And Comments, Joseph Hilbe

Joseph M Hilbe

Errata and Comments for Negative Binomial Regression, 2nd edition

International Astrostatistics Association, Joseph Hilbe

Joseph M Hilbe

Overview of the history, purpose, Council and officers of the International Astrostatistics Association (IAA)

Glme3_Ado_Do_Files, Joseph Hilbe

Joseph M Hilbe

GLME3 ado and do files (116 in total)

Glme3 Data And Adodo Files, Joseph Hilbe

Joseph M Hilbe

A listing of Data Sets and Stata software commands and do files in GLME3 book

Risk, Odds, And Their Ratios, Joseph Hilbe

Joseph M Hilbe

A brief monograph explaining the meaning of the terms, risk, risk ratio, odds, and odds ratio and how to calculate each, together with standard errors and confidence intervals. Stata code is provided showing how all of the terms can be calculated by hand, as well as by using logistic and Poisson models.

Negative Binomial Regression Extensions, Joseph Hilbe

Joseph M Hilbe

Negative Binomial Regression Extensions is an e-book extension of Negative Binomial Regression, 2nd edition, with added R and Stata code, and SAS macros all related to count models.

Suppliment To Logistic Regression Models, Joseph Hilbe

Joseph M Hilbe

No abstract provided.

Basic R Matrix Operations, Joseph Hilbe

Joseph M Hilbe

No abstract provided.

Using R To Create Synthetic Discrete Response Regression Models, Joseph Hilbe

Joseph M Hilbe

The creation of synthetic models allows a researcher to better understand models as well as the bias that can occur when the assumptions upon which a model is based is violated. This article provides R code that can be used or amended to create a variety of discrete response regression models.

Nbr2 Stata Ado-Do Files, Joseph Hilbe

Joseph M Hilbe

No abstract provided.

Errata Negative Binomial Regression 1st Edition 1st Print, Joseph Hilbe

Joseph M Hilbe

Errata for the first edition and printing of Negative Binomal Regression, August 2007. Many of the items listed here were corrected in the 2008 second printing.

Creation Of Synthetic Discrete Response Regression Models, Joseph Hilbe

Joseph M Hilbe

The development and use of synthetic regression models has proven to assist statisticians in better understanding bias in data, as well as how to best interpret various statistics associated with a modeling situation. In this article I present code that can be easily amended for the creation of synthetic binomial, count, and categorical response models. Parameters may be assigned to any number of predictors (which are shown as continuous, binary, or categorical), negative binomial heterogeneity parameters may be assigned, and the number of levels or cut points and values may be specified for ordered and unordered categorical response models. I …

Modeling Future Record Performances In Athletics, Joseph Hilbe

Joseph M Hilbe

No abstract provided.

Lrm Revision To Ch 2.1, Joseph Hilbe

Joseph M Hilbe

Rewording of part Ch 2.1 of Logistic Regression Models

Logistic Regression Using R, Joseph Hilbe

Joseph M Hilbe

R code and output for examples in Logistic Regression Models, Chapman & Hall/CRC (2009)

The Black Swan: Praise And Criticism, Peter H. Westfall, Joseph M. Hilbe

Joseph M Hilbe

No abstract provided.

A Review Of Limdep 9.0 And Nlogit 4.0, Joseph Hilbe

Joseph M Hilbe

No abstract provided.

Mathematica 5.2: A Review, Joseph Hilbe

Joseph M Hilbe

No abstract provided.

A Review Of Stata 9.0, Joseph Hilbe

Joseph M Hilbe

No abstract provided.

Poicen.Sas : Censored Poisson Regression, Joseph Hilbe, Gordon Johnston

Joseph M Hilbe

SAS Macro to estimate censored Poisson data, using method of Hilbe. See Hilbe, Joseph M (2011), Negative Binomial Regression, 2nd ed (Cambridge University Press)

Derivation Of A Scaled Binomial As An Instance Of A General Discrete Exponential Distribution, Joseph Hilbe

Joseph M Hilbe

No abstract provided.

Generalized Linear Models: Software Implementation And The Structure Of A General Power-Link Based Glm Algorithm, Joseph Hilbe

Joseph M Hilbe

Generalized linear modeling (GLM) is currently undergoing a renaissance. The number of software packages offering GLM capability grows each year and as a partial consequence one finds an increased number of research endeavors being modeled using GLM methodology. On the other hand, there have likewise been an increasing number of requests to vendors by users of statistical packages to include GLM facilities amid other offerings. The overall effect has been a near 300 percent increase in GLM programs over the past four years.

I shall discuss the nature of generalized linear models followed by an examination of how they have …

Log-Negative Binomial Regression As A Generalized Linear Model, Joseph Hilbe

Joseph M Hilbe

The negative binomial (NB) is a member of the exponential family of discrete probability distributions. The nature of the distribution is itself well understood, but its contribution to regression modeling, in particular as a generalized linear model (GLM), has not been appreciated. The mathematical properties of the negative binomial are derived and GLM algorithms are developed for both the canonical and log form. Geometric regression is seen as an instance of the NB. The log forms of both may be effectively used to model types of POisson-overdispersed count data. A GLM-type algorithm is created for a general log-negative binomial regression …

The Pseudo-Problem Of Induction, Joseph Hilbe

Joseph M Hilbe

Paper I delivered at the IVth International Congress for Logic, Methodology, and Philosophy of Science held in Bucharest, Romania in 1971.