Displaying similar documents to “Minimax prediction of the difference of sample distribution functions”

Minimax prediction under random sample size

Alicja Jokiel-Rokita (2002)

Applicationes Mathematicae

Similarity:

A class of minimax predictors of random variables with multinomial or multivariate hypergeometric distribution is determined in the case when the sample size is assumed to be a random variable with an unknown distribution. It is also proved that the usual predictors, which are minimax when the sample size is fixed, are not minimax, but they remain admissible when the sample size is an ancillary statistic with unknown distribution.

A model for proportions with medical applications

Saralees Nadarajah (2007)

Applicationes Mathematicae

Similarity:

Data that are proportions arise most frequently in biomedical research. In this paper, the exact distributions of R = X + Y and W = X/(X+Y) and the corresponding moment properties are derived when X and Y are proportions and arise from the most flexible bivariate beta distribution known to date. The associated estimation procedures are developed. Finally, two medical data sets are used to illustrate possible applications.

Computing the distribution of a linear combination of inverted gamma variables

Viktor Witkovský (2001)

Kybernetika

Similarity:

A formula for evaluation of the distribution of a linear combination of independent inverted gamma random variables by one-dimensional numerical integration is presented. The formula is direct application of the inversion formula given by Gil–Pelaez [gil-pelaez]. This method is applied to computation of the generalized p -values used for exact significance testing and interval estimation of the parameter of interest in the Behrens–Fisher problem and for variance components in balanced...

Three methods for constructing reference prior distributions.

Eusebio Gómez Sánchez-Manzano, Miguel A. Gómez Villegas (1990)

Revista Matemática de la Universidad Complutense de Madrid

Similarity:

Three methods are proposed for constructing reference prior densities for certain biparametric distribution families. These densities represent approximations to the Bayesian concept of noninformative distribution.

Improving predictive distributions.

Morris H. DeGroot (1980)

Trabajos de Estadística e Investigación Operativa

Similarity:

Consider a sequence of decision problems S, S, ... and suppose that in problem S the statistician must specify his predictive distribution F for some random variable X and make a decision based on that distribution. For example, X might be the return on some particular investment and the statistician must decide whether or not to make that investment. The random variables X, X, ... are assumed to be independent and completely unrelated. It is also assumed that each predictive distribution...