AIC-TYPE MODEL SELECTION CRITERION FOR MULTIVARIATE LINEAR REGRESSION WITH A FUTURE EXPERIMENT

نویسندگان

چکیده

برای دانلود باید عضویت طلایی داشته باشید

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

a new type-ii fuzzy logic based controller for non-linear dynamical systems with application to 3-psp parallel robot

abstract type-ii fuzzy logic has shown its superiority over traditional fuzzy logic when dealing with uncertainty. type-ii fuzzy logic controllers are however newer and more promising approaches that have been recently applied to various fields due to their significant contribution especially when the noise (as an important instance of uncertainty) emerges. during the design of type- i fuz...

15 صفحه اول

A Consistency Property of the AIC for Multivariate Linear Models

It is common knowledge that the Akaike’s information criterion (AIC) is not a consistent model selection criterion. This inconsistency property has been confirmed from an asymptotic selection probability evaluated from a large-sample asymptotic framework. However, when a high-dimensional asymptotic framework, such that the dimension of the response variables and the sample size are approaching ...

متن کامل

Selection of Model Selection Criteria for Multivariate Ridge Regression

In the present study, we consider the selection of model selection criteria for multivariate ridge regression. There are several model selection criteria for selecting the ridge parameter in multivariate ridge regression, e.g., the Cp criterion and the modified Cp (MCp) criterion. We propose the generalized Cp (GCp) criterion, which includes Cp andMCp criteria as special cases. The GCp criterio...

متن کامل

A WEIGHTED LINEAR REGRESSION MODEL FOR IMPERCISE RESPONSE

A weighted linear regression model with impercise response and p-real explanatory variables is analyzed. The LR fuzzy random variable is introduced and a metric is suggested for coping with this kind of variables. A least square solution for estimating the parameters of the model is derived. The result are illustrated by the means of some case studies.

متن کامل

ذخیره 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: JOURNAL OF THE JAPAN STATISTICAL SOCIETY

سال: 1997

ISSN: 1882-2754,1348-6365

DOI: 10.14490/jjss1995.27.135