Log-linear, logistic model fitting and local score statistics for cluster detection with covariate adjustments

2010 ◽  
Vol 30 (1) ◽  
pp. 91-100
Author(s):  
Hock Peng Chan ◽  
I-Ping Tu
Keyword(s):  
Logistic Model ◽  
Model Fitting ◽  
Cluster Detection ◽  
Local Score ◽  
Score Statistics ◽  
Log Linear ◽  
Linear Logistic Model

scholarly journals Using JAGS for Bayesian Cognitive Diagnosis Modeling: A Tutorial

2019 ◽  
Vol 44 (4) ◽  
pp. 473-503 ◽  
Author(s):  
Peida Zhan ◽  
Hong Jiao ◽  
Kaiwen Man ◽  
Lijun Wang
Keyword(s):  
Gibbs Sampler ◽  
Logistic Model ◽  
Structural Model ◽  
Higher Order ◽  
Unified Model ◽  
Cognitive Diagnosis ◽  
Cognitive Diagnosis Models ◽  
Log Linear ◽  
Linear Logistic Model ◽  
Or Gate

In this article, we systematically introduce the just another Gibbs sampler (JAGS) software program to fit common Bayesian cognitive diagnosis models (CDMs) including the deterministic inputs, noisy “and” gate model; the deterministic inputs, noisy “or” gate model; the linear logistic model; the reduced reparameterized unified model; and the log-linear CDM (LCDM). Further, we introduce the unstructured latent structural model and the higher order latent structural model. We also show how to extend these models to consider polytomous attributes, the testlet effect, and longitudinal diagnosis. Finally, we present an empirical example as a tutorial to illustrate how to use JAGS codes in R.

A Marketer'S Guide to Log-Linear Models for Qualitative Data Analysis

1980 ◽  
Vol 44 (3) ◽  
pp. 40-51 ◽  
Author(s):  
Wayne S. DeSarbo ◽  
David K. Hildebrand
Keyword(s):  
Data Analysis ◽  
Qualitative Data ◽  
Potential Problem ◽  
Linear Models ◽  
Model Fitting ◽  
Qualitative Data Analysis ◽  
Chi Square ◽  
Stepwise Procedures ◽  
Log Linear

This article presents a brief description of the development and use of log-linear models. Current research in areas of screening effects, partitioning chi-square statistics, stepwise procedures, and model fitting and selection is discussed. An analysis of inherent model ambiguities is provided, identifying potential problem areas for marketing applications.

Improvement of fire danger modelling with geographically weighted logistic model

2014 ◽  
Vol 23 (8) ◽  
pp. 1130 ◽  
Author(s):  
Haijun Zhang ◽  
Pengcheng Qi ◽  
Guangmeng Guo
Keyword(s):  
Spatial Autocorrelation ◽  
Logistic Model ◽  
Model Fitting ◽  
Logistic Models ◽  
Principal Component ◽  
Predictive Performance ◽  
Fire Danger ◽  
Local Models ◽  
Global Models ◽  
North East

Global models dominate historical documents on fire danger modelling. However, local variations may exist in the relationships between fire presence and fire-influencing factors. In this study, 50 fire danger models (10 global logistic models and 40 geographically weighted logistic models, i.e. local models), were developed to model daily fire danger in Heilongjiang province in north-east China and cross-validation was performed to evaluate the predictive performance of the various developed models. In modelling, multi-temporal spatial sampling and repeated random sub-sampling were applied to obtain 10 groups of training sub-samples and inner testing sub-samples. For each of the 10 groups of training sub-samples, principal component analysis, in which muticollinearity among variables can be removed, was used to create nine principal components that were then employed as covariates to develop one global logistic model and four geographically weighted logistic models. Compared to global models, all local models showed better model fitting, less spatial autocorrelation of residuals and more desirable modelling of fire presence. In particular, not only was local spatial variation in fire–environment relationships accounted for in the adaptive Gaussian geographically weighted logistic models, but spatial autocorrelation of residuals was significantly reduced to acceptable levels, indicating strong inferential performance.

scholarly journals Analysis of consumer behavior in a small size market entity: case study for Vlora District, Albania

2018 ◽  
Vol 6 (03) ◽  
Author(s):  
Elmira Kushta ◽  
Dode Prenga ◽  
Fatmir Memaj
Keyword(s):  
Consumer Behavior ◽  
Size Effect ◽  
Logistic Model ◽  
Model Fitting ◽  
The Other ◽  
Actual State ◽  
Local Market ◽  
Market Area ◽  
Significance Level

In standard econometric application all variables are analyzed statistically before being used in mathematical models. In this framework we considered non-stationary distribution as an starting procedure on the study of consumer behavior in a local market area whereof non-homogeneity of buyers and small size effect could be present. By evaluation of the degree of non-stationary of the actual state for particular variable as observed, we hope to be able to estimate and interpret the model outcomes. Assuming the non-stationary of variables as indicator of the overall stet itself, we argue that the state where observation were made is non-stationary too, and for that reason, models are expected to not fit well. In the other hand, by dropping the significance level in model fitting process we expect to count for this instability whereas the model remains valid. Herewith, the logistic model for consumer behavior in our system is applied and calculated using significance level 0.85-0.90. Under such limiting constraint assumption we identified the variables that mostly affected the proportion between expense categories and the characteristics of the expenses that mostly describe the market consumer behavior in the unity studied. We hope that methodically this procedure could be helpful for other similar market or socio-metric study as well.

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