2 3 Preface Many classification procedures are based on variable selection methodologies. This master thesis concentrates on continuous variable selection procedures based on the shrinkage principle. Generally, we would like to find sparse prediction rules for multi-class classification problems such that in increases the prediction accuracy but also the interpretability of the obtained predict...

BACKGROUND Performance measurement is essential to the management of health care organizations to which efficiency is per se a vital indicator. Present study accordingly aims to measure the efficiency of hospitals employing two distinct methods. METHODS Data Envelopment Analysis and Pabon Lasso Model were jointly applied to calculate the efficiency of all general hospitals located in Iranian ...

BACKGROUND Because multiple loci control complex diseases, there is great interest in testing markers simultaneously instead of one by one. In this paper, we applied two model selection algorithms: the stochastic search variable selection (SSVS) and the least absolute shrinkage and selection operator (LASSO) to two quantitative phenotypes related to rheumatoid arthritis (RA). RESULTS The Gene...

Sparsity or parsimony of statistical models is crucial for their proper interpretations, as in sciences and social sciences. Model selection is a commonly used method to find such models, but usually involves a computationally heavy combinatorial search. Lasso (Tibshirani, 1996) is now being used as a computationally feasible alternative to model selection. Therefore it is important to study La...

In the sparse linear regression setting, we consider testing the significance of the predictor variable that enters the current lasso model, in the sequence of models visited along the lasso solution path. We propose a simple test statistic based on lasso fitted values, called the covariance test statistic, and show that when the true model is linear, this statistic has an Exp(1) asymptotic dis...

I briefly report on some unexpected results that I obtained when optimizing the model parameters of the Lasso. In simulations with varying observations-to-variables ratio n/p, I typically observe a strong peak in the test error curve at the transition point n/p = 1. This peaking phenomenon is well-documented in scenarios that involve the inversion of the sample covariance matrix, and as I illus...

I use the adaptive elastic net in a Bayesian framework and test its forecasting performance against lasso, adaptive lasso and elastic net (all used in a Bayesian framework) in a series of simulations, as well as in an empirical exercise for macroeconomic Euro area data. The results suggest that elastic net is the best model among the four Bayesian methods considered. Adaptive lasso, on the othe...

SUMMARY: Structural equation models are well-developed statistical tools for multivariate data with latent variables. Recently, much attention has been given to developing structural equation models that account for nonlinear relationships between the endogenous latent variables, the covariates, and the exogenous latent variables. [Guo et al. (2012)], developed a semiparametric structural equat...

In order to clarify the variable selection of Lasso, Lasso is compared with two other methods AIC and stagewise forward. First, that AIC, it discovered has a wider application range than AIC. The data simulation shows under orthonormal design consistent can be solved by using algorithm stepwise selection, removed variables appear again nonorthonormal design, isn’t We continue compare between fo...