On partitioning conditional independence model for three-way contingency tables

2011 ◽  
Vol 6 (4) ◽  
pp. 349-355
Author(s):  
Kiyotaka Iki ◽  
Kouji Tahata ◽  
Sadao Tomizawa
Keyword(s):  
Conditional Independence ◽  
Contingency Tables ◽  
Independence Model ◽  
Conditional Independence Model

Analysis of Two-Way Contingency Tables : Log-Linear Models and Dual Scaling

1987 ◽  
Vol 26 (03) ◽  
pp. 104-108
Author(s):  
M. A. A. Moussa
Keyword(s):  
Linear Models ◽  
Contingency Tables ◽  
Real Data ◽  
Categorical Variables ◽  
Bayes Estimators ◽  
Dual Scaling ◽  
Independence Model ◽  
Optimal Weights ◽  
Log Linear ◽  
Female Nurses

SummaryThe paper focuses upon the measurement of association in two-way contingency tables, using the log-linear models and dual scaling approaches. The former comprises [1] the use of pseudo-Bayes estimators to remove zeros, [2] fitting the resulting smoothed array to all possible configurations of log-linear models, [3] fitting the quasi-independence model to detect anomalous cells that caused deviation from the null-independence model. The latter includes [1] estimation of the optimal weights that maximize the canonical correlation between the two categorical variables by an optimization iterative method, [2] testing the discriminability of the estimated scoring scheme. The two approaches were applied to a set of real data for the study of the association between maternal age at marriage and types of reproductive wastage in a sampling survey conducted in the population of female nurses in Kuwait.

scholarly journals On Causal Identification under Markov Equivalence

2019 ◽  
Author(s):  
Amin Jaber ◽  
Jiji Zhang ◽  
Elias Bareinboim
Keyword(s):  
Causal Effect ◽  
Causal Structure ◽  
Qualitative Description ◽  
Experimental Distribution ◽  
Independence Model ◽  
Causal Diagrams ◽  
Outcome Set ◽  
Conditional Independence Model ◽  
Markov Equivalence ◽  
Ancestral Graph

In this work, we investigate the problem of computing an experimental distribution from a combination of the observational distribution and a partial qualitative description of the causal structure of the domain under investigation. This description is given by a partial ancestral graph (PAG) that represents a Markov equivalence class of causal diagrams, i.e., diagrams that entail the same conditional independence model over observed variables, and is learnable from the observational data. Accordingly, we develop a complete algorithm to compute the causal effect of an arbitrary set of intervention variables on an arbitrary outcome set.

scholarly journals Decomposition of Parsimonious Independence Model Using Pearson, Kendall and Spearman's Correlations for Two-Way Contingency Tables

2018 ◽  
Vol 7 (3) ◽  
pp. 105
Author(s):  
Kiyotaka Iki ◽  
Shun Sato ◽  
Sadao Tomizawa
Keyword(s):  
Contingency Tables ◽  
Correlation Coefficients ◽  
Test Statistics ◽  
Association Model ◽  
Ordered Categories ◽  
Independence Model ◽  
Linear Association

For two-way contingency tables with ordered categories, Tomizawa (1992) considered the parsimonious Linear-by-Linear association model. This model can be described in terms of fewer parameters than the Linear-by-Linear association model (Agresti, 1983). The purpose of this paper is (i) to define the parsimonious independence model, (ii) to show the parsimonious independence model holds if and only if the parsimonious Linear-by-Linear association model holds and the each one of various correlation coefficients is equal to zero, and (iii) show the statistic for testing the parsimonious independence model is asymptotically equivalent to the sum of test statistics for the decomposed models.

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