In a multiple linear regression model, there are instances where one has to update the regression parameters. In such models as new data become available, by adding one row to the design matrix, the least-squares estimates for the parameters must be updated to reflect the impact of the new data. We will modify two existing methods of calculating regression coefficients in multiple linear regres...

We introduce the local composite quantile regression (LCQR) to causal inference in discontinuity (RD) designs. Kai, Li and Zou study efficiency property of LCQR, while we show that its nice boundary performance translates accurate estimation treatment effects RD under a variety data generating processes. Moreover, propose bias-corrected standard error-adjusted t-test for inference, which leads ...

this paper deals with ridge estimation of fuzzy nonparametric regression models using triangular fuzzy numbers. this estimation method is obtained by implementing ridge regression learning algorithm in the la- grangian dual space. the distance measure for fuzzy numbers that suggested by diamond is used and the local linear smoothing technique with the cross- validation procedure for selecting t...

a pedigree file consisting of 5860 individuals, 167 sires and 1582 dams collected at makooei sheep breeding station (msbs) during a period of 24 years (1990 to 2013) was used to calculate the inbreeding coefficients to reveal any probable effects of inbreeding (f) on the studied traits. the studied traits were classified to the five main groups including body weight, kleiber ratio, body measure...

background: poverty and low socioeconomic status are the most important reasons of increasing the global burden of tuberculosis, not only in developing countries but also in developed countries for particular groups. the purpose of this study was to assess the association between socioeconomic factors and the number of tuberculosis patients using quantile regression for count data.   methods: t...