Casino self‐exclusion programmes: A review of the issues 1

Abstract

Casino self‐exclusion is a procedure by which individuals can have themselves banned from entering a casino. One of the purposes of this paper is to present information about the availability and features of these programmes. A second purpose is to make recommendations about how to best operate them based on cross‐jurisdictional analysis and lessons from the addiction literature. The first section of the paper describes the typical casino self‐exclusion programme, outlining the features common to most policies. The second section provides a detailed overview of the programmes operating in Canada in order to give the reader an appreciation of the procedural variations that exist. The third section discusses the effectiveness of self‐exclusion programmes. Finally, the fourth section contains recommendations on ways to improve effectiveness. When properly implemented, self‐exclusion can be a valuable tool in helping to curb problem gambling.     

A Sample Size Formula for a Non-Central t Test

An elementary method of proof of the mode, median, and mean inequality is given for skewed, unimodal distributions of continuous random variables. A proof of the inequality for the gamma, F, and beta random variables is sketched.     

Editor's Report

There are two common methods for statistical inference on 2 × 2 contingency tables. One is the widely taught Pearson chi-square test, which uses the well-known χ²statistic. The chi-square test is appropriate for large sample inference, and it is equivalent to the Z-test that uses the difference between the two sample proportions for the 2 × 2 case. Another method is Fisher’s exact test, which evaluates the likelihood of each table with the same marginal totals. This article mathematically justifies that these two methods for determining extreme do not completely agree with each other. Our analysis obtains one-sided and two-sided conditions under which a disagreement in determining extreme between the two tests could occur. We also address the question whether or not their discrepancy in determining extreme would make them draw different conclusions when testing homogeneity or independence. Our examination of the two tests casts light on which test should be trusted when the two tests draw different conclusions.     

Parameter estimation in nonlinear regression models 1

Fisher information contained in record values, inter-record times and their concomitants from a sample of fixed size is derived in general and explicit expressions are deduced for some specific known bivariate classes of distributions. A comparison between fixed sampling and inverse sampling schemes with equal number of records and concomitants is also carried out. We also consider parameter estimation based on bivariate records and a small simulation study is done.