Prediction of leaf number by linear regression models in cassava

Planning Level Regression Models for Prediction of the Number of Crashes on Urban Arterials in Bangladesh

In most of the developing countries, the metropolitan organizations do not assess the safety consequences of alternative transportation systems and one of the reasons is the lack of suitable methodology. The goal of this paper is to develop practical tools for assessing safety consequences of arterial roads in the context of long-term urban transportation plans in Dhaka city, the capital of Ban...

Robust Estimation in Linear Regression with Molticollinearity and Sparse Models

‎One of the factors affecting the statistical analysis of the data is the presence of outliers‎. ‎The methods which are not affected by the outliers are called robust methods‎. ‎Robust regression methods are robust estimation methods of regression model parameters in the presence of outliers‎. ‎Besides outliers‎, ‎the linear dependency of regressor variables‎, ‎which is called multicollinearity...

A NOTE ON EVALUATION OF FUZZY LINEAR REGRESSION MODELS BY COMPARING MEMBERSHIP FUNCTIONS

Kim and Bishu (Fuzzy Sets and Systems 100 (1998) 343-352) proposeda modification of fuzzy linear regression analysis. Their modificationis based on a criterion of minimizing the difference of the fuzzy membershipvalues between the observed and estimated fuzzy numbers. We show that theirmethod often does not find acceptable fuzzy linear regression coefficients andto overcome this shortcoming, pr...

Prediction in Functional Linear Regression

There has been substantial recent work on methods for estimating the slope function in linear regression for functional data analysis. However, as in the case of more conventional finite-dimensional regression, much of the practical interest in the slope centers on its application for the purpose of prediction, rather than on its significance in its own right. We show that the problems of slope...

Prediction of Percent Change in Linear Regression by Correlated Variables