Record Details

A Study on Logistic Regression Modeling for Classification in Agriculture

KrishiKosh

View Archive Info
 
 
Field Value
 
Title A Study on Logistic Regression Modeling for Classification in Agriculture
 
Creator ARPAN BHOWMIK
 
Contributor Ramasubramanian, V.
 
Subject wastes, planting, cowpeas, food wastes, extraction, composting, crops, sowing, fruits, developmental stages
 
Description T-8105
Classification and prediction in agricultural systems are quite useful for planning
purposes. In this study, logistic regression modeling has been employed for
classification purposes in the field of agriculture. The data pertain to the area of
agricultural ergonomics with dependent variable as the presence or absence of
discomfort for the farm labourers in operating farm machineries along with
associated quantitative and qualitative regressors. From the different possible
variable subset datasets, only appropriate logistic regression models that best fit these
datasets have been selected for further study. Relevant goodness of fit and predictive
ability measures has been utilized for evaluating the fitted models. A single best
regressor i.e. load given to the farm machinery during operation has also been
identified by employing variable selection based on collinearity diagnostics and
stepwise logistic regression. Comparison made between the length of confidence
intervals of estimates from Maximum Likelihood Estimation (MLE) and quadratic
bootstrap methods upon the original sample using the single best regressor revealed
that the latter is better than the former as quadratic bootstrap estimates had smaller
length of confidence intervals. In addition, resampling based estimation method viz.
Quadratic Bootstrap has been applied for estimating the unknown parameters in a
simple logistic regression model under a simulation study whose parameter estimates
had less bias than that obtained using the conventional MLE procedure without
increase in their corresponding estimated variances. Also when the classificatory
performances of the logistic regression models (using the best regressor) fitted using
both MLE and Quadratic Bootstrap approaches are compared, the results came out to
be at par under the two approaches. Classifications of the hold-out datasets revealed
that results obtained using logistic regression models were found to be better when
compared to those obtained from discriminant function analysis method. Moreover,
when comparisons were made among the MLE based logistic regression models, the
model with the single best regressor came out to be the best. The study revealed that
logistic regression modeling can be employed as a viable alternative for classification
purposes in the field of agriculture.
 
Date 2016-12-16T12:13:38Z
2016-12-16T12:13:38Z
2009
 
Type Thesis
 
Identifier http://krishikosh.egranth.ac.in/handle/1/90556
 
Format application/pdf
 
Publisher IARI, Indian Agricultural Statistics Research Institute