678 research outputs found
lawstat: An R Package for Law, Public Policy and Biostatistics
We present a new R software package lawstat that contains statistical tests and procedures that are utilized in various litigations on securities law, antitrust law, equal employment and discrimination as well as in public policy and biostatistics. Along with the well known tests such as the Bartels test, runs test, tests of homogeneity of several sample proportions, the Brunner-Munzel tests, the Lorenz curve, the Cochran-Mantel-Haenszel test and others, the package contains new distribution-free robust tests for symmetry, robust tests for normality that are more sensitive to heavy-tailed departures, measures of relative variability, Levene-type tests against trends in variances etc. All implemented tests and methods are illustrated by simulations and real-life examples from legal cases, economics and biostatistics. Although the package is called lawstat, it presents implementation and discussion of statistical procedures and tests that are also employed in a variety of other applications, e.g., biostatistics, environmental studies, social sciences and others, in other words, all applications utilizing statistical data analysis. Hence, name of the package should not be considered as a restriction to legal statistics. The package will be useful to applied statisticians and "quantitatively alert practitioners" of other subjects as well as an asset in teaching statistical courses.
Comparing Survival Curves Using Rank Tests
Survival times of patients can be compared using rank tests in various experimental setups, including the two-sample case and the case of paired data. Attention is focussed on two frequently occurring complications in medical applications: censoring and tail alternatives. A review is given of the author's recent work on a new and simple class of censored rank tests. Various models for tail alternatives are discussed and the relation to censoring is demonstrated
Effect of pooling samples on the efficiency of comparative studies using microarrays
Many biomedical experiments are carried out by pooling individual biological
samples. However, pooling samples can potentially hide biological variance and
give false confidence concerning the data significance. In the context of
microarray experiments for detecting differentially expressed genes, recent
publications have addressed the problem of the efficiency of sample-pooling,
and some approximate formulas were provided for the power and sample size
calculations. It is desirable to have exact formulas for these calculations and
have the approximate results checked against the exact ones. We show that the
difference between the approximate and exact results can be large. In this
study, we have characterized quantitatively the effect of pooling samples on
the efficiency of microarray experiments for the detection of differential gene
expression between two classes. We present exact formulas for calculating the
power of microarray experimental designs involving sample pooling and technical
replications. The formulas can be used to determine the total numbers of arrays
and biological subjects required in an experiment to achieve the desired power
at a given significance level. The conditions under which pooled design becomes
preferable to non-pooled design can then be derived given the unit cost
associated with a microarray and that with a biological subject. This paper
thus serves to provide guidance on sample pooling and cost effectiveness. The
formulation in this paper is outlined in the context of performing microarray
comparative studies, but its applicability is not limited to microarray
experiments. It is also applicable to a wide range of biomedical comparative
studies where sample pooling may be involved.Comment: 8 pages, 1 figure, 2 tables; to appear in Bioinformatic
lawstat: An R Package for Law, Public Policy and Biostatistics
We present a new R software package lawstat that contains statistical tests and procedures that are utilized in various litigations on securities law, antitrust law, equal employment and discrimination as well as in public policy and biostatistics. Along with the well known tests such as the Bartels test, runs test, tests of homogeneity of several sample proportions, the Brunner-Munzel tests, the Lorenz curve, the Cochran-Mantel-Haenszel test and others, the package contains new distribution-free robust tests for symmetry, robust tests for normality that are more sensitive to heavy-tailed departures, measures of relative variability, Levene-type tests against trends in variances etc. All implemented tests and methods are illustrated by simulations and real-life examples from legal cases, economics and biostatistics. Although the package is called lawstat, it presents implementation and discussion of statistical procedures and tests that are also employed in a variety of other applications, e.g., biostatistics, environmental studies, social sciences and others, in other words, all applications utilizing statistical data analysis. Hence, name of the package should not be considered as a restriction to legal statistics. The package will be useful to applied statisticians and "quantitatively alert practitioners" of other subjects as well as an asset in teaching statistical courses
A simple correction to remove the bias of the gini coefficient due to grouping
Abstract-We propose a first-order bias correction term for the Gini index to reduce the bias due to grouping. It depends on only the number of individuals in each group and is derived from a measurement error framework. We also provide a formula for the remaining second-order bias. Both Monte Carlo and EU and U.S. empirical evidence show that the first-order correction reduces a considerable share of the bias, but that some remaining second-order bias is increasing in the variance. We propose a procedure that addresses the remaining second-order bias by using additional information
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