G*Power (Erdfelder, Faul, & Buchner, 1996) was designed as a general stand-alone power analysis program for statistical tests commonly used in social and behavioral research. G*Power 3 is a major extension of, and improvement over, the previous versions. It runs on widely used computer platforms (i.e., Windows XP, Windows Vista, and Mac OS X 10.4) and covers many different statistical tests of thet, F, and χ2 test families. In addition, it includes power analyses forz tests and some exact tests. G*Power 3 provides improved effect size calculators and graphic options, supports both distribution-based and design-based input modes, and offers all types of power analyses in which users might be interested. Like its predecessors, G*Power 3 is free.

https://link.springer.com/content/pdf/10.3758/BF03193146.pdf

G*Power 3: A flexible statistical power analysis program for the social, behavioral, and biomedical sciences

In the early 1980s, a strategy for graphical representation of multivariate (multi-species) abundance data was introduced into marine ecology by, among others, Field, et al. (1982). A decade on, it is instructive to: (i) identify which elements of this often-quoted strategy have proved most useful in practical assessment of community change resulting from pollution impact; and (ii) ask to what extent evolution of techniques in the intervening years has added self-consistency and comprehensiveness to the approach. The pivotal concept has proved to be that of a biologically-relevant definition of similarity of two samples, and its utilization mainly in simple rank form, for example ‘sample A is more similar to sample B than it is to sample C’. Statistical assumptions about the data are thus minimized and the resulting non-parametric techniques will be of very general applicability. From such a starting point, a unified framework needs to encompass: (i) the display of community patterns through clustering and ordination of samples; (ii) identification of species principally responsible for determining sample groupings; (iii) statistical tests for differences in space and time (multivariate analogues of analysis of variance, based on rank similarities); and (iv) the linking of community differences to patterns in the physical and chemical environment (the latter also dictated by rank similarities between samples). Techniques are described that bring such a framework into place, and areas in which problems remain are identified. Accumulated practical experience with these methods is discussed, in particular applications to marine benthos, and it is concluded that they have much to offer practitioners of environmental impact studies on communities.

Non-parametric multivariate analyses of changes in community structure

Advances in functional and structural MR image analysis and implementation as FSL.

Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

Co-Planar Stereotaxic Atlas of the Human Brain—3-Dimensional Proportional System: An Approach to Cerebral Imaging, J. Talairach, P. Tournoux. Georg Thieme Verlag, New York (1988), 122 pp., 130 figs. DM 268

Contingency tables and the chi-square test.- 2 x 2 Contingency tables.- r x c Contingency tables.- Multidimensional tables.- Log-linear models for contingency tables.- Some special types of contingency table.

The analysis of contingency tables

1 General introduction.- 1.1 Introduction.- 1.2 Some applications of finite mixture distributions.- 1.3 Definition.- 1.4 Estimation methods.- 1.4.1 Maximum likelihood.- 1.4.2 Bayesian estimation.- 1.4.3 Inversion and error minimization.- 1.4.4 Other methods.- 1.4.5 Estimating the number of components.- 1.5 Summary.- 2 Mixtures of normal distributions.- 2.1 Introduction.- 2.2 Some descriptive properties of mixtures of normal distributions.- 2.3 Estimating the parameters in normal mixture distributions.- 2.3.1 Method of moments estimation.- 2.3.2 Maximum likelihood estimation.- 2.3.3 Maximum likelihood estimates for grouped data.- 2.3.4 Obtaining initial parameter values for the maximum likelihood estimation algorithms.- 2.3.5 Graphical estimation techniques.- 2.3.6 Other estimation methods.- 2.4 Summary.- 3 Mixtures of exponential and other continuous distributions.- 3.1 Exponential mixtures.- 3.2 Estimating exponential mixture parameters.- 3.2.1 The method of moments and generalizations.- 3.2.2 Maximum likelihood.- 3.3 Properties of exponential mixtures.- 3.4 Other continuous distributions.- 3.4.1 Non-central chi-squared distribution.- 3.4.2 Non-central F distribution.- 3.4.3 Beta distributions.- 3.4.4 Doubly non-central t distribution.- 3.4.5 Planck's distribution.- 3.4.6 Logistic.- 3.4.7 Laplace.- 3.4.8 Weibull.- 3.4.9 Gamma.- 3.5 Mixtures of different component types.- 3.6 Summary.- 4 Mixtures of discrete distributions.- 4.1 Introduction.- 4.2 Mixtures of binomial distributions.- 4.2.1 Moment estimators for binomial mixtures.- 4.2.2 Maximum likelihood estimators for mixtures of binomial distributions.- 4.2.3 Other estimation methods for mixtures of binomial distributions.- 4.3 Mixtures of Poisson distributions.- 4.3.1 Moment estimators for mixtures of Poisson distributions.- 4.3.2 Maximum likelihood estimators for a Poisson mixture.- 4.4 Mixtures of Poisson and binomial distributions.- 4.5 Mixtures of other discrete distributions.- 4.6 Summary.- 5 Miscellaneous topics.- 5.1 Introduction.- 5.2 Determining the number of components in a mixture.- 5.2.1 Informal diagnostic tools for the detection of mixtures.- 5.2.2 Testing hypotheses on the number of components in a mixture.- 5.3 Probability density function estimation.- 5.4 Miscellaneous problems.- 5.5 Summary.- References.

Finite Mixture Distributions

Principles Of Multivariate Analysis Multivariate analysis (MVA) is based on the principles of multivariate statistics, which involves observation and analysis of more than one statistical outcome variable at a time.Typically, MVA is used to address the situations where multiple measurements are made on each experimental unit and the relations among these measurements and their structures are important.

Principles of multivariate analysis

The Cambridge dictionary of statistics , The Cambridge dictionary of statistics , کتابخانه دیجیتال جندی شاپور اهواز

/pdf/the-cambridge-dictionary-of-statistics-4udydfxnzu.pdf

The Cambridge dictionary of statistics

Encyclopedia of statistics in behavioral science , Encyclopedia of statistics in behavioral science , کتابخانه دیجیتال جندی شاپور اهواز

Brian Everitt

Papers

The analysis of contingency tables

Finite Mixture Distributions

Principles of multivariate analysis

The Cambridge dictionary of statistics

Encyclopedia of statistics in behavioral science