a weighted linear regression model with impercise response and p-real explanatory variables is analyzed. the lr fuzzy random variable is introduced and a metric is suggested for coping with this kind of variables. a least square solution for estimating the parameters of the model is derived. the result are illustrated by the means of some case studies.

Cox proportional hazards model is a commonly used model in providing hazard ratio to compare survival times of two population groups. The exponentiated linear regression part of the model describes the effects of explanatory variables on hazard ratio. PROC PHREG is a SAS procedure that implements the Cox model and provides the hazard ratio estimate. The estimate is interpreted as the percent ch...

Multiple-regression analyses have been performed on 32 years of total ozone column data that was spatially gridded with a 1× 1.5 resolution. The total ozone data consist of the MSR (Multi Sensor Reanalysis; 1979–2008) and 2 years of assimilated SCIAMACHY (SCanning Imaging Absorption spectroMeter for Atmospheric CHartographY) ozone data (2009–2010). The two-dimensionality in this data set allows...

Given a population described by p explanatory and one dependent categorical variables, we assume that the dependent variable defines a partition of the population into g groups. Discriminant Analysis studies the relation between the p explanatory variables and the dependent variable finding the subset of variables that has the most predictive power. Generally, in categorical discriminant analys...

In this-first every study for Chile, a neural network based hierarchical modelling approach is proposed for forecasting domestic waste generation for the whole country. Over 30 global variables from the 342 communes (municipalities) in the country were analysed extensively using statistical tools that led to 5 significant explanatory variables: population, percentage of urban population, years ...

A MIDAS regression involves a dependent variable observed at a low frequency and independent variables observed at a higher frequency. This paper relates a true high frequency data generating process, where also the dependent variable is observed (hypothetically) at the high frequency, with a MIDAS regression. It is shown that a correctly specified MIDAS regression usually includes lagged depen...

the study investigates the impact of taxation on the nigerian economy for the period 1994 -2012.the dependent variables used in the model includes: gross domestic product (gdp) as a parameter for measuring economic growth, inflation and unemployment. the objective is this study is to determine how taxation affects these macroeconomic variables. to avoid spurious results, the data set collected ...

It is shown how one can effectively use microdata in modelling the change over time in an aggregate (e.g. mean consumption expenditure) of a large and heterogeneous population. The starting point of our aggregation analysis is a specification of explanatory variables on the micro-level. Typically, some of these explanatory variables are observable and others are unobservable. Based on certain h...

Linear structural error-in-variables models with univariate observations are revisited for studying modified least squares estimators of the slope and intercept. New marginal central limit theorems (CLT’s) are established for these estimators, assuming the existence of four moments for the measurement errors and that the explanatory variables are in the domain of attraction of the normal law. T...

It is shown how one can effectively use microdata in modelling the change over time in an aggregate (e.g. mean consumption expenditure) of a large and heterogeneous population. The starting point of our aggregation analysis is a specification of explanatory variables on the micro-level. Typically, some of these explanatory variables are observable and others are unobservable. Based on certain h...