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The paper presents a multi-disciplinary decentralized integrated modeling approach (DIMA) for comprehensive modeling production and logistics networks. The DIMA allows the implementing of the principle of throughout modeling 'conceptual model → mathematical model → software'. In the paper, the DIMA is illustrated on the problem of collaborative network (CN) dynamical synthesis and reconfiguration. This problem is described in a comprehensive way on the conceptual and mathematical levels. This description allows interlinking synthesis and analysis issues of CN planning and control taking into account system elements' activity, uncertainty factors, and structure dynamics. The paper reports a novel approach for comprehensive multi-disciplinary modeling distributed large-scale business systems with decentralized decision-making and control. In this paper, the main features of the DIMA are summarized based on recently developed partial models and algorithms for a large spectrum of CN management issues. [ABSTRACT FROM AUTHOR]

A two-step procedure is described in which AID is applied first to a multiway contingency table to isolate those variables (and levels within variable) that best account for variance in the criterion. A logit analysis follows for estimating parameters for prediction purposes. The method is described and applied illustratively to a set of data on usage of an AT&T telecommunications service. [ABSTRACT FROM AUTHOR]

Computer simulations are excellent tools for understanding the evolutionary and genetic consequences of complex processes whose interactions cannot be analytically predicted. Simulations have traditionally been used in population genetics by a fairly small community with programming expertise, but the recent availability of dozens of sophisticated, customizable software packages for simulation now makes simulation an accessible option for researchers in many fields. The in silico genetic data produced by simulations, along with greater availability of population-genomics data, are transforming genetic epidemiology, anthropology, evolutionary and population genetics and conservation. In this Review of the state-of-the-art of simulation software, we identify applications of simulations, evaluate simulator capabilities, provide a guide for their use and summarize future directions. [ABSTRACT FROM AUTHOR]

Researchers often compare the relationship between an outcome and covariate for two or more groups by evaluating whether the fitted regression curves differ significantly. When they do, researchers need to determine the “significance region,” or the values of the covariate where the curves significantly differ. In analysis of covariance (ANCOVA), the Johnson-Neyman procedure can be used to determine the significance region; for the hierarchical linear model (HLM), the Miyazaki and Maier (M-M) procedure has been suggested. However, neither procedure can assume nonnormally distributed data. Furthermore, the M-M procedure produces biased (downward) results because it uses the Wald test, does not control the inflated Type I error rate due to multiple testing, and requires implementing multiple software packages to determine the significance region. In this article, we address these limitations by proposing solutions for determining the significance region suitable for generalized linear (mixed) model (GLM or GLMM). These proposed solutions incorporate test statistics that resolve the biased results, control the Type I error rate using Scheffé’s method, and uses a single statistical software package to determine the significance region. [ABSTRACT FROM PUBLISHER]

The article presents a study on the use of SAS PROC GLIMMIX in bivariate random-effects meta-analysis of sensitivities and specificities. It compares the implemented SAS PROC GLIMMIX to the indirect hierarchical summary receiver operating characteristics (HSROC) approach using the 50 meta-analyses published 2 x 2 count data. It shows that bivariate GLIMMIX approach delivers results that are almost similar with the indirect HSROC approach and thus offer a direct method for such analysis.

One of the difficulties students from basic courses often show in learning probability and statistics at the university is understanding how the continuous normal probability distribution approximates the discrete binomial probability distribution. In this paper a didactic proposal is presented consisting in two algorithms, very easy to use, developed with software R which is completely free. These algorithms aim to introduce students in basic concepts of normal approximation to the binomial distribution by means of graphical representations and tables. They allow an active participation of student in the learning process. [ABSTRACT FROM AUTHOR]

Abstract: This paper proposes a new algorithm for K-medoids clustering which runs like the K-means algorithm and tests several methods for selecting initial medoids. The proposed algorithm calculates the distance matrix once and uses it for finding new medoids at every iterative step. To evaluate the proposed algorithm, we use some real and artificial data sets and compare with the results of other algorithms in terms of the adjusted Rand index. Experimental results show that the proposed algorithm takes a significantly reduced time in computation with comparable performance against the partitioning around medoids. [Copyright &y& Elsevier]

We propose a new nonpaxametric conditional cumulative distribution function kernel estimator that admits a mix of discrete and categorical data along with an associated nonparametric conditional quantile estimator. Bandwidth selection for kernel quantile regression remains an open topic of re,arch. We employ a conditional probability density function-based bandwidth selector proposed by Hall, Racine, and Li that can automatically remove irrelevant variables and has impressive performance in this setting. We provide theoretical underpinnings including rates of convergence and limiting distributions. Simulations demonstrate that this approach performs quite well relative to its peers; two illustrative examples serve to underscore its value in applied settings. [ABSTRACT FROM AUTHOR]

Abstract: This paper presents an algorithm that generates families of mathematical models with nonlinear parameters, and includes the study of linear models, based on the experimental data of the intervening variables. The implementation of this algorithm has been named polymodel and is based on the application of the Gauss–Newton algorithm for obtaining the parameters of nonlinear models [Verdú F. Un Algoritmo para la construcción múltiple de modelos matemáticos no lineales y el estudio de su estabilidad. Doctoral Tesis. Universidad de Alicante, 2004]. One of its characteristics is a search among different nonlinear models within the parameters; unlike the methods found in the scientific literature [Camacho Rosales J. Estadística con SPSS para windows. Ed. Ra-Ma, 2002; Mathsoft Inc. Splus-4. Guide to Statistics. Seattle, 1997], the user does not intervene in their generation. A pruning criteria has also been introduced that is based on the stability analysis of models generated from perturbations, applying studies carried out by the authors and published in [Verdú F, Villacampa Y. A computer program for a Monte Carlo analysis of sensitivity in equations of environmental modelling obtained from experimental data. Advances in Engineering Software, 2002]. Object-oriented Pascal has been used in Delphi 6. [Copyright &y& Elsevier]

In this paper we investigate the combined effects of quantization and clipping on high-order function neural networks (HOFNN). Statistical models are used to analyze the effects of quantization in a digital implementation. We analyze the performance degradation caused as a function of the number of fixed-point and floating-point quantization bits under the assumption of different probability distributions for the quantized variables, and then compare the training performance between situations with and without weight clipping. We establish and analyze the relationships for a true nonlinear neuron between inputs and outputs bit resolution, training and quantization methods, network order and performance degradation, all based on statistical models, and for on-chip and off-chip training. Our experimental simulation results verify the presented theoretical analysis. [ABSTRACT FROM AUTHOR]

This note summarizes developments of thegenepop software since its first description in 1995, and in particular those new to version 4.0: an extended input format, several estimators of neighbourhood size under isolation by distance, new estimators and confidence intervals for null allele frequency, and less important extensions to previous options.genepop now runs under Linux as well as under Windows, and can be entirely controlled by batch calls. [ABSTRACT FROM AUTHOR]

Abstract: Triangular contingency tables are a special class of incomplete contingency tables. Association and independence models are used to analyze such tables. This paper presents and compares some methods including the uniform association model and the quasi-independence model. These models can be described in terms of the association parameters for the analysis of triangular contingency tables having ordered categories. A computer program is developed for the analysis of quasi-independence model for positive (negative) likelihood dependence. The sign test, which is a nonparametric test of the independence against the likelihood ratio dependence, is also examined. These methods are applied to the disability ratings of stroke patients data. Effects of the structural zeros on the results are also discussed. [Copyright &y& Elsevier]

Recent improvements in text entry error rats measurement have enabled the running of text entry experiments in which subjects are free to correct errors (or not) as they transcribe a presented string. In these "unconstrained" experiments, it is no longer necessary to force subjects to unnaturally maintain synchronicity with presented text for the sake of performing overall error rate calculations. However, the calculation of character-level error rates, which can be trivial in artificially constrained evaluations, is far more complicated in unconstrained text entry evaluations because it is difficult to infer a subject's intention at every character. For this reason, prior character level error analyses for unconstrained experiments have only compared presented and transcribed strings, not input streams. But input streams are rich sources of character-level error information, since they contain all of the text entered (and erased) by a subject. The current work presents an algorithm for the automated analysis of character-level errors in input streams for unconstrained text entry evaluations. It also presents new character-level metrics that can aid method designers in refining text entry methods. To exercise these metrics, we perform two analyses on data from an actual text entry experiment. One analysis, available from the prior work, uses only presented and transcribed strings. The other analysis uses input stream, as described in the current work. The results confirm that input stream error analysis yields richer information for the same empirical data. To facilitate the use of these new analyses, we offer pseudocode and downloadable software for performing unconstrained text entry experiments and analyzing data. [ABSTRACT FROM AUTHOR]

Reviews the computer software "SigmaStat 3.1," from SPSS Inc.

Statistical packages have been used for decades to analyze large datasets or to perform mathematically intractable statistical methods. These packages are not capable of working with random variables having arbitrary distributions. This article presents a prototype probability package named APPL (A Probability Programming Language) that can be used to manipulate random variables. Examples illustrate its use. A current version of the software can be obtained by contacting the third author at leemis@math.wm.edu. [ABSTRACT FROM AUTHOR]

Presents information on a study that examined a contingency enumeration based approach to evaluate the voltage stability of a power system. Overview of the mathematical models used in the study; Presentation of the case studies; Data collection and analysis.

Focuses on the benefits of the nonparametric tests of categorical data. Discussion of the contingency table tests implemented in the SAS programming language.

Examines the validity of the asymptotically distribution-free (ADF) test statistic. Importance of the weight matrix estimation in determining the accuracy of ADF; Use of existing literature and computer software to get an unbiased estimate; Factors affecting the performance of the ADF method.

Focuses on the computer assisted K-fold crossvalidation technique in canonical analysis. Methods to reduce contamination of canonical variates; Correlation between the predicted and unpredicted variates; Effectiveness of the algorithm used.

Presents a review of hypothesis testing and inference in balanced mixed models in statistics. Performance of popular mainframe computer packages; BMDP, 1990 release; SAS version 6; SPSS version 4.0; Text and reference books.

Presents an algorithm for testing homogeneity in a two by two contingency table. Description and purpose of the test algorithm; Structure of the subroutine table; Execution time to compute the critical regions.

The article presents examples of visualization, focusing on ways in which knowledge-assisted visualization may be distinguished from information-assisted visualization. The concepts of data abstraction quality, local statistical complexity, and isosurface topology are discussed in terms of visualization of information, while the example of viewpoint mutual information is cited as a way of embedding knowledge in a visualization system.

Evaluates the statistical inference software StatXact 6.0 from Cytel Software Corp. Systems requirement; Areas of application; New features.

Presents information on (Rater Agreement/Categorical Data) RATCAT, a FORTRAN program using contingency-table format to calculate indices of rater reliability. Operations of RATCAT; Versions of the program; Usefulness of the program for analyzing data from pilot research and teaching rater-agreement statistics.