Pearson chi-square estimator and test for log-linear models with expected frequencies subject to linear constraints

1989 ◽  
Vol 8 (2) ◽  
pp. 175-177 ◽  
Author(s):  
Douglas G. Bonett
Keyword(s):  
Linear Models ◽  
Linear Constraints ◽  
Chi Square ◽  
Log Linear

scholarly journals Risk factors for intradomiciliary infestation by the Chagas disease vector Triatoma dimidiatain Jutiapa, Guatemala

2009 ◽  
Vol 25 (suppl 1) ◽  
pp. S83-S92 ◽  
Author(s):  
Dulce Maria Bustamante ◽  
Carlota Monroy ◽  
Sandy Pineda ◽  
Antonieta Rodas ◽  
Xochitl Castro ◽  
...  
Keyword(s):  
Risk Factors ◽  
Socioeconomic Status ◽  
Chagas Disease ◽  
Linear Models ◽  
Control Strategies ◽  
Chi Square ◽  
Disease Vector ◽  
Log Linear ◽  
Chi Square Analysis

Seventeen variables were evaluated as possible risk factors for the intradomiciliary infestation with Triatoma dimidiata in 644 houses in Jutiapa, Guatemala. During 2004 the houses were assessed for vector presence and evaluated for hygiene, cluttering, material comfort, construction conditions and number of inhabitants, among other factors. Chi-square analysis detected significant associations between vector presence and eight variables related to domestic sanitary and construction conditions. Log-linear models showed that regardless of the age of the house, the odds of vector presence were 4.3 and 10 times lower in houses with a good socioeconomic status compared with poor and very poor houses respectively. Log-linear models also pointed to a greater chance of vector presence when walls lacked plastering (3.85 times) or walls had low quality-incomplete plastering (4.56 times), compared with walls that were completely plastered. Control strategies against T. dimidiata should include the introduction of better-quality but inexpensive plastering formulations and better sanitation practices should also be promoted among the population. Such control strategies should not only reduce or eliminate infestation, but also prevent vector reinfestation.

scholarly journals Asymptotic Results for Multinomial Models

Symmetry ◽  
2021 ◽  
Vol 13 (11) ◽  
pp. 2173
Author(s):  
Isaac Akoto ◽  
João T. Mexia ◽  
Filipe J. Marques
Keyword(s):  
Linear Models ◽  
Confidence Regions ◽  
Asymptotic Results ◽  
Chi Square ◽  
Normal Distributions ◽  
Limit Distributions ◽  
Multinomial Models ◽  
Multidimensional Contingency Table ◽  
Log Linear ◽  
Key Parameter

In this work, we derived new asymptotic results for multinomial models. To obtain these results, we started by studying limit distributions in models with a compact parameter space. This restriction holds since the key parameter whose components are the probabilities of the possible outcomes have non-negative components that add up to 1. Based on these results, we obtained confidence ellipsoids and simultaneous confidence intervals for models with normal limit distributions. We then studied the covariance matrices of the limit normal distributions for the multinomial models. This was a transition between the previous general results and on the inference for multinomial models in which we considered the chi-square tests, confidence regions and non-linear statistics—namely log-linear models with two numerical applications to those models. Namely, our approach overcame the hierarchical restrictions assumed to analyse the multidimensional contingency table.

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.

scholarly journals Social-Psychological Determinants Of Anxiety Disorder (SAD) Among Youth (With Reference To Karachi University)

2014 ◽  
Vol 8 (1) ◽  
pp. 161-176
Author(s):  
Rana Saba Sultan ◽  
Ambreen Akber ◽  
Erum Zarrar
Keyword(s):  
Anxiety Disorder ◽  
Linear Models ◽  
Strong Relationship ◽  
Research Approach ◽  
Exploratory Research ◽  
Chi Square ◽  
Psychological Determinants ◽  
Chi Square Test ◽  
And Gender ◽  
Log Linear

The present study is “A study of Causes of Anxiety Disorder (SAD) among youth of Karachi University; deals with behavior patterns and expected to reflect on the type of society and its attitude towards SAD. The present research is an exploratory research. This study is based on the concept of level of sadness among youth of Karachi University. The mixed research approach quantitative and qualitative was adopted. The population of the study was Karachi University students; 120 students were selected through purposive sampling. Five hypotheses were formulated, from which three were accepted and two were rejected. Hence it is proved that there is a relationship between level of sadness with parents and gender, and this is also proved that there is a relationship between sadness and marital status and hence it is also proved that there is a relationship between level of troubled with appearance and gender, it is also found that there is a strong relationship between problem in decision making and cultural background and level of feeling of sadness and age. To test the hypotheses, statistical methods were applied to the data described in the contingency tables. The chi-square test was applied to test the existence of the relationship between two variables of hypothesis, and if the null hypothesis is rejected then the log linear models were also used to measure the degree of relationship. The hypotheses were then finally interpreted and the results were obtained.

When and why are log-linear models self-normalizing?

2015 ◽  
Author(s):  
Jacob Andreas ◽  
Dan Klein
Keyword(s):  
Linear Models ◽  
Log Linear

Log-Linear Models with Dependent Spatial Data

1983 ◽  
Vol 15 (6) ◽  
pp. 801-813 ◽  
Author(s):  
B Fingleton
Keyword(s):  
Spatial Data ◽  
Statistical Models ◽  
Linear Models ◽  
Square Lattice ◽  
Categorical Variables ◽  
Systematic Sampling ◽  
Pairwise Correlations ◽  
Independent Observations ◽  
Intersection Points ◽  
Log Linear

Log-linear models are an appropriate means of determining the magnitude and direction of interactions between categorical variables that in common with other statistical models assume independent observations. Spatial data are often dependent rather than independent and thus the analysis of spatial data by log-linear models may erroneously detect interactions between variables that are spurious and are the consequence of pairwise correlations between observations. A procedure is described in this paper to accommodate these effects that requires only very minimal assumptions about the nature of the autocorrelation process given systematic sampling at intersection points on a square lattice.

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