✦ LIBER ✦

Outliers in Multivariate Regression Models

✍ Scribed by Muni S. Srivastava; Dietrich von Rosen

Publisher: Elsevier Science
Year: 1998
Tongue: English
Weight: 279 KB
Volume: 65
Category: Article
ISSN: 0047-259X
DOI: 10.1006/jmva.1997.1729

No coin nor oath required. For personal study only.

✦ Synopsis

Likelihood ratio tests for detecting a single outlier in multivariate linear models are considered, where an observation is called an outlier if there has been a shift in the mean. The test statistics are the maximum of n nonindependent statistics, where n is the number of observations. Relevant distributions to use upper and lower Bonferroni's inequalities are given.

📜 SIMILAR VOLUMES

A Note on Maximum Likelihood Ratio Test

A Note on Maximum Likelihood Ratio Test of No Outliers in Regression Models

✍ Prof. S. R. Paul 📂 Article 📅 1986 🏛 John Wiley and Sons 🌐 English ⚖ 180 KB 👁 2 views

A derivation of the maximum likelihood ratio test for testing no outliere in regreeeion models h given ueing the method of WETEXEILL (1981, pp. 106-107) for estimating the regreeeion parsmetere. This method h eseentially eimilar to the one outlined in B a s m and Lmwm (1978, p. 283), although by our

Outlier Detection in Regression Models w

Outlier Detection in Regression Models with ARIMA Errors using Robust Estimates

✍ A. M. Bianco; M. García Ben; E. J. Martínez; V. J. Yohai 📂 Article 📅 2001 🏛 John Wiley and Sons 🌐 English ⚖ 130 KB

## Abstract A diagnostic procedure for detecting additive and innovation outliers as well as level shifts in a regression model with ARIMA errors is introduced. The procedure is based on a robust estimate of the model parameters and on innovation residuals computed by means of robust filtering. A M

Finding causes of outliers in multivaria

Finding causes of outliers in multivariate environmental data

✍ Forest C. Garner; Martin A. Stapanian; Kirk E. Fitzgerald 📂 Article 📅 1991 🏛 John Wiley and Sons 🌐 English ⚖ 438 KB 👁 1 views

Forecasting volatility with outliers in

Forecasting volatility with outliers in GARCH models

✍ Amélie Charles 📂 Article 📅 2008 🏛 John Wiley and Sons 🌐 English ⚖ 115 KB

## Abstract In this paper, we detect and correct abnormal returns in 17 French stocks returns and the French index CAC40 from additive‐outlier detection method in GARCH models developed by Franses and Ghijsels (1999) and extended to innovative outliers by Charles and Darné (2005). We study the effe

A regression model for multivariate rand

A regression model for multivariate random length data

✍ Huiman X. Barnhart; Andrzej S. Kosinski; Allan R. Sampson 📂 Article 📅 1999 🏛 John Wiley and Sons 🌐 English ⚖ 109 KB 👁 2 views

Multivariate random length data occur when we observe multiple measurements of a quantitative variable and the variable number of these measurements is also an observed outcome for each experimental unit. For example, for a patient with coronary artery disease, we may observe a number of lesions in

LAD Regression for Detecting Outliers in

LAD Regression for Detecting Outliers in Response and Explanatory Variables

✍ Yadolah Dodge 📂 Article 📅 1997 🏛 Elsevier Science 🌐 English ⚖ 284 KB

Least absolute deviations regression resists outliers in the response variable but is relatively sensitive to outlying observations in the explanatory variables. In this paper a simple solution is proposed to overcome this problem. This is achieved by minimizing the absolute values of vertical and h