Selection of model selection criteria for multivariate ridge regression
نویسندگان
چکیده
منابع مشابه
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 کاملVariable Selection for Multivariate Logistic Regression Models
In this paper, we use multivariate logistic regression models to incorporate correlation among binary response data. Our objective is to develop a variable subset selection procedure to identify important covariates in predicting correlated binary responses using a Bayesian approach. In order to incorporate available prior information, we propose a class of informative prior distributions on th...
متن کاملConsistency Properties of Model Selection Criteria in Multiple Linear Regression
This paper concerns the asymptotic properties of a class of criteria for model selection in linear regression models, which covers the most well known criteria as e.g. MALLOWS' Cp, CV (cross-validation), GCV ( generalized cross-validation), AKAIKE's AIC and FPE as well as SCHWARZ' BIC. These criteria are shown to be consistent in the sense of selecting the true or larger models, assuming i.i.d....
متن کاملAn investigation of model selection criteria for technical analysis of moving average
Moving averages are one of the most popular and easy-to-use tools available to a technical analyst, and they also form the building blocks for many other technical indicators and overlays. Building a moving average (MA) model needs determining four factors of (1) approach of issuing signals, (2) technique of calculating MA, (3) length of MA, and (4) band. After a literature re...
متن کاملCausal & Non-Causal Feature Selection for Ridge Regression
In this paper we investigate the use of causal and non-causal feature selection methods for linear classifiers in situations where the causal relationships between the input and response variables may differ between the training and operational data. The causal feature selection methods investigated include inference of the Markov Blanket and inference of direct causes and of direct effects. Th...
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: Hiroshima Mathematical Journal
سال: 2013
ISSN: 0018-2079
DOI: 10.32917/hmj/1368217951