Trosset (Indiana U.) presents a self-contained introduction to the methods of

statistical inference for those students who are comfortable with a mathematical approach.

His topics include random variables, joint distributions, variance and covariance, moment generating functions, analysis of some important distributions, and

statistical inference. ([umlaut] Ringgold, Inc., Portland, OR)

Statistical inference; the minimum distance approach.

Using non-full-rank design matrices and numerous models, Monahan covers the linear least squares problem, estimability and least squares estimators, the Gauss-Markov model, distributional theory,

statistical inference, topics in testing (such as orthogonal polynomials and contrasts), variance components and mixed models, and the multivariate linear model.

Classical

statistical inference for coefficient alpha is well developed.

Neglecting to define how to measure evidence is a significant failure for any proposed theory of

statistical inference, declares Evans.

systems,

statistical inference, regression analysis, and experience working with large datasets;

Introduction to the theory of

statistical inference.

The authors introduce order statistics and applications, then cover basic distribution theory, including joint distribution of two order statistics, discrete order statistics, including joint probability mass functions and distributions of the range, order statistics from specific distributions, including Bernoulli and Poisson distributions moment relations, bounds, approximations, characterizations using order statistics, order statistics in

statistical inference, asymptotic theory, including central and intermediate order statistics and record values.

SEDAL aims to develop the next generation of

statistical inference methods for EO data analysis.

The topics are compositional data and their sample space, the Aitchison geometry, coordinate representation, exploratory data analysis, random compositions,

statistical inference, linear models, and compositional processes.

They treat all variables--manifest and latent, continuous or categorical--as random variables, then subsequent analysis is done wholly within the realm of the probability calculus and the theory of

statistical inference. Previous editions were published in 1987 and 1999; this edition accounts for the significant changes in statistics since them, primarily the increase in computing power, which makes some approaches practical that were not before.