Displaying similar documents to “The application of minimax fit in linear model and its extension in generalised linear models.”

An adaptive method of estimation and outlier detection in regression applicable for small to moderate sample sizes

Brenton R. Clarke (2000)

Discussiones Mathematicae Probability and Statistics

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In small to moderate sample sizes it is important to make use of all the data when there are no outliers, for reasons of efficiency. It is equally important to guard against the possibility that there may be single or multiple outliers which can have disastrous effects on normal theory least squares estimation and inference. The purpose of this paper is to describe and illustrate the use of an adaptive regression estimation algorithm which can be used to highlight outliers, either single...

Fitting a linear regression model by combining least squares and least absolute value estimation.

Sira Allende, Carlos Bouza, Isidro Romero (1995)

Qüestiió

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Robust estimation of the multiple regression is modeled by using a convex combination of Least Squares and Least Absolute Value criterions. A Bicriterion Parametric algorithm is developed for computing the corresponding estimates. The proposed procedure should be specially useful when outliers are expected. Its behavior is analyzed using some examples.

Study of Bootstrap Estimates in Cox Regression Model with Delayed Entry

Silvie Bělašková, Eva Fišerová, Sylvia Krupičková (2013)

Acta Universitatis Palackianae Olomucensis. Facultas Rerum Naturalium. Mathematica

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In most clinical studies, patients are observed for extended time periods to evaluate influences in treatment such as drug treatment, approaches to surgery, etc. The primary event in these studies is death, relapse, adverse drug reaction, or development of a new disease. The follow-up time may range from few weeks to many years. Although these studies are long term, the number of observed events is small. Longitudinal studies have increased the importance of statistical methods for time-to...

A note on robust estimation in logistic regression model

Tadeusz Bednarski (2016)

Discussiones Mathematicae Probability and Statistics

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Computationally attractive Fisher consistent robust estimation methods based on adaptive explanatory variables trimming are proposed for the logistic regression model. Results of a Monte Carlo experiment and a real data analysis show its good behavior for moderate sample sizes. The method is applicable when some distributional information about explanatory variables is available.

Some inferential questions in regard to analysing two-way Layouts and associated linear model theory and practice

Brenton R. Clarke, Antony G. Monaco (2004)

Discussiones Mathematicae Probability and Statistics

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In analysing a well known data set from the literature which can be thought of as a two-way layout it transpires that a robust adaptive regression approach for identifying outliers fails to be sensitive enough to detect the possible interchange of two observations. On the other hand if one takes the classical approach of diagnostic checking one may also stop too early and be satisfied with a model that falls short of a more detailed analysis that takes account of heteroscedasticity in...

Aspects of multivariate regression.

Philip J. Brown (1980)

Trabajos de Estadística e Investigación Operativa

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Important features of multivariate linear regression are emphasised and a selection of prior distributions discussed. Priors used by Brown and Zidek (1978) lead them to a class of 'empirical' Bayes shrinkage estimates. The strength of shrinkage is examined with respect to an election forecasting example where observations obtain one after another.

On the Equivalence between Orthogonal Regression and Linear Model with Type-II Constraints

Sandra Donevska, Eva Fišerová, Karel Hron (2011)

Acta Universitatis Palackianae Olomucensis. Facultas Rerum Naturalium. Mathematica

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Orthogonal regression, also known as the total least squares method, regression with errors-in variables or as a calibration problem, analyzes linear relationship between variables. Comparing to the standard regression, both dependent and explanatory variables account for measurement errors. Through this paper we shortly discuss the orthogonal least squares, the least squares and the maximum likelihood methods for estimation of the orthogonal regression line. We also show that all mentioned...