Multivariate regression with consecutively added dependent variables
نویسندگان
چکیده
منابع مشابه
Wavelets for Nonparametric Stochastic Regression with Pairwise Negative Quadrant Dependent Random Variables
We propose a wavelet based stochastic regression function estimator for the estimation of the regression function for a sequence of pairwise negative quadrant dependent random variables with a common one-dimensional probability density function. Some asymptotic properties of the proposed estimator are investigated. It is found that the estimators have similar properties to their counterparts st...
متن کاملPhylogenetic logistic regression for binary dependent variables.
We develop statistical methods for phylogenetic logistic regression in which the dependent variable is binary (0 or 1) and values are nonindependent among species, with phylogenetically related species tending to have the same value of the dependent variable. The methods are based on an evolutionary model of binary traits in which trait values switch between 0 and 1 as species evolve up a phylo...
متن کاملNon-parametric regression for binary dependent variables
Finite-sample properties of non-parametric regression for binary dependent variables are analyzed. Non parametric regression is generally considered as highly variable in small samples when the number of regressors is large. In binary choice models, however, it may be more reliable since its variance is bounded. The precision in estimating conditional means as well as marginal effects is invest...
متن کاملPhylogenetic Regression for Binary Dependent Variables
We compare three methods for phylogenetic regression analyses designed for binary dependent variables (traits with two discrete states) both with each other and with ‘‘standard’’ methods that either ignore phylogenetic relationships or ignore the binary character of the dependent variable. In simulations designed to reveal statistical problems arising in different methods, PLogReg (Ives and Gar...
متن کاملMultivariate Regression with Calibration
We propose a new method named calibrated multivariate regression (CMR) for fitting high dimensional multivariate regression models. Compared to existing methods, CMR calibrates the regularization for each regression task with respect to its noise level so that it is simultaneously tuning insensitive and achieves an improved finite-sample performance. Computationally, we develop an efficient smo...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Linear Algebra and its Applications
سال: 2005
ISSN: 0024-3795
DOI: 10.1016/j.laa.2005.06.033