2,188 research outputs found
On eigenvalues, case deletion and extremes in regression
This paper presents an approximation for assessing the effect of deleting an observation in the eigenvalues of the correlation matrix of a multiple linear regression modelo Applications in connection with the detection of collinearityinfluential
observations are explored
On the behaviour of residual plots in regression
Properties of least squares versus robust regression residual plots are compared under a common set of assumptions
A note on the multivariate box-cox transformation to normality
We study some aspects of the multivariate Box-Cox transformation to normality which have received little attention in the literature
Diagnostics and robust estimation in multivariate data transformations
This paper presents a method for detecting multivariate outliers which might be distorting theı estimation of a transformation to normality. A robust estimator of the transformation parameter is also proposed
A quantile approach to the box-cox transformation in random samples
This paper presents an alternative approach to the likelihood methods for estimating the parameter A in the Box-Cox family of transformations when the data arise from a random sample. The method is based on a representation of the quantile function of the variable under consideration. Theoretical properties of the method, its practical applications and comparison with the likelihood approach are studied
Student and School Indicators for Youth in California's Central Valley
Provides a statistical portrait of elementary, secondary, and postsecondary education in the Central Valley. Examines trends in school resources, course enrollment, and student achievement in the region, and compares to trends in the rest of the state
A discriminant rule under transformation
We present a new rule for discriminating among continuous populations which are not multivariate normal. The basic idea is to construct the sample maximum likelihood discriminant rule after transforming the data by a suitable multivariate transformation to normalit
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