scholarly journals Definição de Processos de Software baseada em uma Arquitetura de Componentes de Processo

2011 ◽  
Author(s):  
Luiz Carlos M. Ribeiro ◽  
Cristiane S. Ramos ◽  
Kamilla Crozara ◽  
Hilmer R. Neri ◽  
Eusyar Alves ◽  
...  
Keyword(s):  
Process Engineering ◽  
Model Specification ◽  
Meta Model

Neste trabalho é apresentado um modelo de processo padrão elaborado a partir de uma arquitetura de componentes de Processo de Software baseada no meta-modelo Software & Systems Process Engineering Meta-Model Specification (SPEM), que permite definir processos de desenvolvimento de software e sistemas e seus componentes. A partir do modelo de processo padrão foi realizada a reutilização de componentes de processo para o estabelecimento de um processo em uso no contexto de desenvolvimento de jogos eletrônicos utilizando mecanismos relacionados aos tipos de variabilidade de processo.

scholarly journals Catalogo de Padrões de Definição de Processos de Software

2012 ◽  
Author(s):  
Luiz Carlos M. Ribeiro ◽  
Kamilla H. Crozara ◽  
Hilmer R. Neri ◽  
Andre Luiz P. M. Lanna ◽  
Cristiane S. Ramos ◽  
...  
Keyword(s):  
Software Process ◽  
Process Engineering ◽  
Model Specification ◽  
Process Definition ◽  
Meta Model

Neste trabalho e apresentada a especificação de um catálogo de padrões de definição de processos de software (software process definition patterns), que consiste em um conjunto de soluções genéricas e reutilizáveis que podem ser aplicadas em classes de problemas conhecidos e recorrentes. O catálogo foi definido no contexto de uma arquitetura de componentes de processos de software reutilizáveis, baseada no meta-modelo Software & Systems Process Engineering Meta-Model Specification (SPEM). Adicionalmente, exemplos de uso do catálogo são apresentados.

Uses and applications of Software & Systems Process Engineering Meta-Model process models. A systematic mapping study

2013 ◽  
Vol 25 (9) ◽  
pp. 999-1025 ◽  
Author(s):  
Iván Ruiz-Rube ◽  
Juan Manuel Dodero ◽  
Manuel Palomo-Duarte ◽  
Mercedes Ruiz ◽  
David Gawn
Keyword(s):  
Process Engineering ◽  
Process Models ◽  
Software Systems ◽  
Systematic Mapping Study ◽  
Mapping Study ◽  
Systematic Mapping ◽  
Meta Model

Forced Zero Cross-Loading Misspecifications in Measurement Component of Structural Equation Models

Methodology ◽  
2014 ◽  
Vol 10 (4) ◽  
pp. 138-152 ◽  
Author(s):  
Hsien-Yuan Hsu ◽  
Susan Troncoso Skidmore ◽  
Yan Li ◽  
Bruce Thompson
Keyword(s):  
Structural Equation ◽  
Structural Model ◽  
Structural Equation Models ◽  
Equation Model ◽  
Model Fit ◽  
Model Specification ◽  
Simulation Design ◽  
Measurement Models ◽  
Fit Statistics ◽  
Path Coefficients

The purpose of the present paper was to evaluate the effect of constraining near-zero parameter cross-loadings to zero in the measurement component of a structural equation model. A Monte Carlo 3 × 5 × 2 simulation design was conducted (i.e., sample sizes of 200, 600, and 1,000; parameter cross-loadings of 0.07, 0.10, 0.13, 0.16, and 0.19 misspecified to be zero; and parameter path coefficients in the structural model of either 0.50 or 0.70). Results indicated that factor pattern coefficients and factor covariances were overestimated in measurement models when near-zero parameter cross-loadings constrained to zero were higher than 0.13 in the population. Moreover, the path coefficients between factors were misestimated when the near-zero parameter cross-loadings constrained to zero were noteworthy. Our results add to the literature detailing the importance of testing individual model specification decisions, and not simply evaluating omnibus model fit statistics.

A Note on Confidence Intervals and Model Specification

Marketing ZFP ◽  
2019 ◽  
Vol 41 (4) ◽  
pp. 33-42
Author(s):  
Thomas Otter
Keyword(s):  
Confidence Intervals ◽  
Generalized Linear Model ◽  
Statistical Information ◽  
Regression Coefficients ◽  
Model Specification ◽  
Model Parameters ◽  
Exploratory Research ◽  
Empirical Calibration ◽  
Credible Intervals ◽  
Model Structures

Empirical research in marketing often is, at least in parts, exploratory. The goal of exploratory research, by definition, extends beyond the empirical calibration of parameters in well established models and includes the empirical assessment of different model specifications. In this context researchers often rely on the statistical information about parameters in a given model to learn about likely model structures. An example is the search for the 'true' set of covariates in a regression model based on confidence intervals of regression coefficients. The purpose of this paper is to illustrate and compare different measures of statistical information about model parameters in the context of a generalized linear model: classical confidence intervals, bootstrapped confidence intervals, and Bayesian posterior credible intervals from a model that adapts its dimensionality as a function of the information in the data. I find that inference from the adaptive Bayesian model dominates that based on classical and bootstrapped intervals in a given model.

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