Discrepancy and Choice of Reference Subclass in Categorical Regression Models

2014 ◽  
pp. 159-184 ◽  
Author(s):  
Defen Peng ◽  
Gilbert MacKenzie
Keyword(s):  
Regression Models ◽  
Categorical Regression

scholarly journals Population‐calibrated multiple imputation for a binary/categorical covariate in categorical regression models

2018 ◽  
Vol 38 (5) ◽  
pp. 792-808 ◽  
Author(s):  
Tra My Pham ◽  
James R Carpenter ◽  
Tim P Morris ◽  
Angela M Wood ◽  
Irene Petersen
Keyword(s):  
Multiple Imputation ◽  
Regression Models ◽  
Categorical Regression

Conditional and Unconditional Categorical Regression Models with Missing Covariates

Biometrics ◽  
2000 ◽  
Vol 56 (2) ◽  
pp. 384-388 ◽  
Author(s):  
Glen A. Satten ◽  
Raymond J. Carroll
Keyword(s):  
Regression Models ◽  
Missing Covariates ◽  
Categorical Regression

scholarly journals Categorical Regression Models with Optimal Scaling for Predicting Indoor Air Pollution Concentrations inside Kitchens in Nepalese Households

1970 ◽  
Vol 10 ◽  
pp. 205-211 ◽  
Author(s):  
Srijan Lal Shrestha
Keyword(s):  
Air Pollution ◽  
Indoor Air ◽  
Statistical Models ◽  
Regression Models ◽  
Indoor Air Pollution ◽  
Optimal Scaling ◽  
Biomass Fuels ◽  
Categorical Regression ◽  
Poor Ventilation ◽  
Pollution Concentrations

Indoor air pollution from biomass fuels is considered as a potential environmental risk factor in developing countries of the world. Exposure to these fuels have been associated to many respiratory and other ailments such as acute lower respiratory infection, chronic obstructive pulmonary disease, asthma, lung cancer, cataract, adverse pregnancy outcomes, etc. The use of biomass fuels is found to be nearly zero in the developed countries but widespread in the developing countries including Nepal. Women and children are the most vulnerable group since they spend a lot of time inside smoky kitchens with biomass fuel burning, inefficient stove and poor ventilation particularly in rural households of Nepal. Measurements of indoor air pollution through monitoring equipment such as high volume sampler, laser dust monitor, etc are expensive, thus not affordable and practicable to use them frequently. In this context, it becomes imperative to use statistical models instead for predicting air pollution concentrations in household kitchens. The present paper has attempted to contribute in this regard by developing some statistical models specifically categorical regression models with optimal scaling for predicting indoor particulate air pollution and carbon monoxide concentrations based upon a cross-sectional survey data of Nepalese households. The common factors found significant for prediction are fuel type, ventilation situation and house types. The highest estimated levels are found to be for those using solid biomass fuels with poor ventilation and Kachhi houses. The estimated PM10 and CO levels are found to be 3024 μg/m3 and 24115 μg/m3 inside kitchen at cooking time which are 5.2 and 40.40 times higher than the lowest predicted values for those using LPG / biogas and living in Pakki houses with improved ventilation, respectively.Key words: Biomass fuel; Categorical regression; Indoor air pollution; Optimal scaling; Respiratory ailmentsDOI: 10.3126/njst.v10i0.2962Nepal Journal of Science and Technology Vol. 10, 2009 Page: 205-211 

Analyzing infant mortality with geoadditive categorical regression models: a case study for Nigeria

2004 ◽  
Vol 2 (2) ◽  
pp. 229-244 ◽  
Author(s):  
Samson B Adebayo ◽  
Ludwig Fahrmeir ◽  
Stephan Klasen
Keyword(s):  
Infant Mortality ◽  
Regression Models ◽  
Categorical Regression

Categorical Regression Models

Regression ◽  
2013 ◽  
pp. 325-347 ◽  
Author(s):  
Ludwig Fahrmeir ◽  
Thomas Kneib ◽  
Stefan Lang ◽  
Brian Marx
Keyword(s):  
Regression Models ◽  
Categorical Regression

Standardized estimates from categorical regression models

1995 ◽  
Vol 14 (19) ◽  
pp. 2131-2141 ◽  
Author(s):  
Marshall M. Joffe ◽  
Sander Greenland
Keyword(s):  
Regression Models ◽  
Categorical Regression

Regression Models of Foreign Policy Beliefs

1968 ◽  
Author(s):  
David A. Summers ◽  
Thomas R. Stewart
Keyword(s):  
Foreign Policy ◽  
Regression Models ◽  
Policy Beliefs
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