Search results for "Variable"
showing 10 items of 1674 documents
The Induced Smoothed lasso: A practical framework for hypothesis testing in high dimensional regression.
2020
This paper focuses on hypothesis testing in lasso regression, when one is interested in judging statistical significance for the regression coefficients in the regression equation involving a lot of covariates. To get reliable p-values, we propose a new lasso-type estimator relying on the idea of induced smoothing which allows to obtain appropriate covariance matrix and Wald statistic relatively easily. Some simulation experiments reveal that our approach exhibits good performance when contrasted with the recent inferential tools in the lasso framework. Two real data analyses are presented to illustrate the proposed framework in practice.
Clusters of effects curves in quantile regression models
2018
In this paper, we propose a new method for finding similarity of effects based on quantile regression models. Clustering of effects curves (CEC) techniques are applied to quantile regression coefficients, which are one-to-one functions of the order of the quantile. We adopt the quantile regression coefficients modeling (QRCM) framework to describe the functional form of the coefficient functions by means of parametric models. The proposed method can be utilized to cluster the effect of covariates with a univariate response variable, or to cluster a multivariate outcome. We report simulation results, comparing our approach with the existing techniques. The idea of combining CEC with QRCM per…
Nonlinear parametric quantile models
2020
Quantile regression is widely used to estimate conditional quantiles of an outcome variable of interest given covariates. This method can estimate one quantile at a time without imposing any constraints on the quantile process other than the linear combination of covariates and parameters specified by the regression model. While this is a flexible modeling tool, it generally yields erratic estimates of conditional quantiles and regression coefficients. Recently, parametric models for the regression coefficients have been proposed that can help balance bias and sampling variability. So far, however, only models that are linear in the parameters and covariates have been explored. This paper …
Eleccion de variables en regresion lineal un problema de decision
1986
A general structure for the problem of selection of variables in regression is proposed using the decision theory framework. In particular, some results for the choice of the best linear normal homocedastic model are obtained when the main purpose is either to specify the predictive distribution over the response variable or to obtain a point estimate of it. A comparison of our results with the most widespread classical ones is presented
Asymptotic efficiency of the calibration estimator in a high-dimensional data setting
2022
Abstract In a finite population sampling survey, auxiliary information is commonly used to improve the Horvitz-Thompson estimators and calibration has been extensively used by national statistical agencies over the last decades for that purpose. This method enables to make estimators consistent with known totals of auxiliary variables and to reduce variance if the calibration variables are explanatory for the variable of interest. Nowadays, it is not unusual anymore to have high-dimensional auxiliary data sets and adding too much additional calibration variables may increase the variance of calibration estimators. We study in this paper the asymptotic efficiency of the calibration estimator…
Fourth Moments and Independent Component Analysis
2015
In independent component analysis it is assumed that the components of the observed random vector are linear combinations of latent independent random variables, and the aim is then to find an estimate for a transformation matrix back to these independent components. In the engineering literature, there are several traditional estimation procedures based on the use of fourth moments, such as FOBI (fourth order blind identification), JADE (joint approximate diagonalization of eigenmatrices), and FastICA, but the statistical properties of these estimates are not well known. In this paper various independent component functionals based on the fourth moments are discussed in detail, starting wi…
Contributed discussion on article by Pratola
2016
The author should be commended for his outstanding contribution to the literature on Bayesian regression tree models. The author introduces three innovative sampling approaches which allow for efficient traversal of the model space. In this response, we add a fourth alternative.
Residuenanalyse des Unabhängigkeitsmodells Zweier Kategorialer Variablen
1985
For the ‘cellwise’ analysis of independence of two categorial variables, Haberman (1973) proposes the method of ‘adjusted residuals’. Fuchs and Kenett (1980) use (the absolute value of) the maximal adjusted residual as a measure for the deviation from the null hypothesis.
On the Ambiguous Consequences of Omitting Variables
2015
This paper studies what happens when we move from a short regression to a long regression (or vice versa), when the long regression is shorter than the data-generation process. In the special case where the long regression equals the data-generation process, the least-squares estimators have smaller bias (in fact zero bias) but larger variances in the long regression than in the short regression. But if the long regression is also misspecified, the bias may not be smaller. We provide bias and mean squared error comparisons and study the dependence of the differences on the misspecification parameter.
On the ambiguous consequences of omitting variables
2015
This paper studies what happens when we move from a short regression to a long regression (or vice versa), when the long regression is shorter than the data-generation process. In the special case where the long regression equals the data-generation process, the least-squares estimators have smaller bias (in fact zero bias) but larger variances in the long regression than in the short regression. But if the long regression is also misspecified, the bias may not be smaller. We provide bias and mean squared error comparisons and study the dependence of the differences on the misspecification parameter.