Bootstrapping for multivariate linear regression models
نویسندگان
چکیده
منابع مشابه
Bootstrapping Conic Multivariate Adaptive Regression Splines (Bcmars)
Bootstrapping is a computer-intensive statistical method which treats the data set as a population and draws samples from it with replacement. This resampling method has wide application areas especially in mathematically intractable problems. In this study, it is used to obtain the empirical distributions of the parameters to determine whether they are statistically significant or not in a spe...
متن کاملEnvelope Models for Parsimonious and Efficient Multivariate Linear Regression
We propose a new parsimonious version of the classical multivariate normal linear model, yielding a maximum likelihood estimator (MLE) that is asymptotically less variable than the MLE based on the usual model. Our approach is based on the construction of a link between the mean function and the covariance matrix, using the minimal reducing subspace of the latter that accommodates the former. T...
متن کاملBootstrapping Principal Component Regression Models
Bootstrap methods can be used as an alternative for cross-validation in regression procedures such as principal component regression (PCR). Several bootstrap methods for the estimation of prediction errors and confidence intervals are presented. It is shown that bootstrap error estimates are consistent with cross-validation estimates but exhibit less variability. This makes it easier to select ...
متن کاملDetecting non-causal artifacts in multivariate linear regression models
We consider linear models where d potential causes X1, . . . , Xd are correlated with one target quantity Y and propose a method to infer whether the association is causal or whether it is an artifact caused by overfitting or hidden common causes. We employ the idea that in the former case the vector of regression coefficients has ‘generic’ orientation relative to the covariance matrix ΣXX of X...
متن کاملValidating Geospatial Regression Models With Bootstrapping
Spatial statistical models have been used extensively in many geospatial and environmental studies over several decades. While being very important, the issues of testing and validation in spatial statistical models are rarely investigated carefully in spatial environmental studies. Often strict theoretical asymptotic assumptions used in those models are left unexplored or unanswered in many st...
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: Statistics & Probability Letters
سال: 2018
ISSN: 0167-7152
DOI: 10.1016/j.spl.2017.11.001