Bootstrapping is a nonparametric approach for evaluating the distribution of a statistic based on random resampling. This article illustrates the bootstrap as an alternative method for estimating the standard errors when the theoretical calculation is complicated or not available in the current software.

Penalized regression methods for simultaneous variable selection and coefficient estimation, especially those based on the lasso of Tibshirani (1996), have received a great deal of attention in recent years, mostly through frequentist models. Properties such as consistency have been studied, and are achieved by different lasso variations. Here we look at a fully Bayesian formulation of the prob...

the present research aims to study the effects of syllabic structure in standard tajiki persian on the pronunciation of standard iranian persian words and the joint loanwords in these languages. this descriptive study used optimality theory, as a recent branch of generative grammar, to analyse the errors. to that end, ten tajiki-speaking persian learners, studying at imam khomeini international...