Article
Publicly Available
The Two Sample Problem for Multiple Categorical Variables
-
A. G. DiRienzo
Published/Copyright:
July 9, 2006
Published Online: 2006-7-9
©2011 Walter de Gruyter GmbH & Co. KG, Berlin/Boston
Articles in the same Issue
- Article
- A Regression Model for Dependent Gap Times
- Statistical Inference for Variable Importance
- Statistical Classification of Abnormal Blood Profiles in Athletes
- Relationship between Derivatives of the Observed and Full Loglikelihoods and Application to Newton-Raphson Algorithm
- The Two Sample Problem for Multiple Categorical Variables
- Application of a Variable Importance Measure Method
- Choice of Monitoring Mechanism for Optimal Nonparametric Functional Estimation for Binary Data
- Approximate Power and Sample Size Calculations with the Benjamini-Hochberg Method
- Estimating a Survival Distribution with Current Status Data and High-dimensional Covariates
- An Improved Akaike Information Criterion for Generalized Log-Gamma Regression Models
- Targeted Maximum Likelihood Learning
- Modeling the Effect of a Preventive Intervention on the Natural History of Cancer: Application to the Prostate Cancer Prevention Trial
Keywords for this article
conditional independence;
family-wise error rate;
genomics;
genotype sequence;
HIV-1 genotype evolution;
multiple hypothesis testing;
simultaneous inference
Articles in the same Issue
- Article
- A Regression Model for Dependent Gap Times
- Statistical Inference for Variable Importance
- Statistical Classification of Abnormal Blood Profiles in Athletes
- Relationship between Derivatives of the Observed and Full Loglikelihoods and Application to Newton-Raphson Algorithm
- The Two Sample Problem for Multiple Categorical Variables
- Application of a Variable Importance Measure Method
- Choice of Monitoring Mechanism for Optimal Nonparametric Functional Estimation for Binary Data
- Approximate Power and Sample Size Calculations with the Benjamini-Hochberg Method
- Estimating a Survival Distribution with Current Status Data and High-dimensional Covariates
- An Improved Akaike Information Criterion for Generalized Log-Gamma Regression Models
- Targeted Maximum Likelihood Learning
- Modeling the Effect of a Preventive Intervention on the Natural History of Cancer: Application to the Prostate Cancer Prevention Trial
