Open AccessBook
The Statistical Analysis of Discrete Data
Thomas J. Santner,Diane E. Duffy +1 more
TLDR
This paper presents a meta-analysis of large sample theory of univariate Discrete Responses and some results from Linear Algebra suggest that the model chosen may be biased towards linear models.Abstract:
1 Introduction.- 2 Univariate Discrete Responses.- 3 Loglinear Models.- 4 Cross-Classified Data.- 5 Univariate Discrete Data with Covariates.- Appendix 1. Some Results from Linear Algebra.- Appendix 2. Maximization of Concave Functions.- Appendix 3. Proof of Proposition 3.3.1 (ii) and (iii).- Appendix 4. Elements of Large Sample Theory.- Problems.- References.- List of Notation.- Index to Data Sets.- Author Index.read more
Citations
More filters
Book
An introduction to categorical data analysis
TL;DR: In this paper, the authors present a tour of categorical data analysis for Contingency Tables and Logit and Loglinear models for contingency tables, as well as generalized linear models for Matched Pairs.
Proceedings Article
Fitting a mixture model by expectation maximization to discover motifs in biopolymers.
Timothy L. Bailey,Charles Elkan +1 more
TL;DR: The algorithm described in this paper discovers one or more motifs in a collection of DNA or protein sequences by using the technique of expectation maximization to fit a two-component finite mixture model to the set of sequences.
Journal ArticleDOI
Approximate is Better than “Exact” for Interval Estimation of Binomial Proportions
Alan Agresti,Brent A. Coull +1 more
TL;DR: For example, this paper showed that using the adjusted Wald test with null rather than estimated standard error yields coverage probabilities close to nominal confidence levels, even for very small sample sizes, and that the 95% score interval has similar behavior as the adjusted-Wald interval obtained after adding two "successes" and two "failures" to the sample.
Journal ArticleDOI
Interval Estimation for a Binomial Proportion
TL;DR: In this paper, the problem of interval estimation of a binomial proportion is revisited, and a number of natural alternatives are presented, each with its motivation and con- text, each interval is examined for its coverage probability and its length.