All a b c d e f g h i j k l m n o p q r s t u v w x y z Other

Previous Page 3 Next

Displaying 41 – 60 of 94

Showing per page

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

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 the data....

Some invariant test procedures for detection of structural changes; behavior under alternatives

Marie Hušková (2001)

Kybernetika

Regression- and scale-invariant M -test procedures for detection of structural changes in linear regression model was developed and their limit behavior under the null hypothesis was studied in Hušková [9]. In the present paper the limit behavior under local alternatives is studied. More precisely, it is shown that under local alternatives the considered test statistics have asymptotically normal distribution.

Some remarks on the individuals-score distance and its applications to statistical inference.

Antonio Miñarro, Josep M. Oller (1992)

Qüestiió

This paper is concerned with the study of some properties of the distance between statistical individuals based on representations on the dual tangent space of a parametric manifold representation of a statistical model. Explicit expressions for distances are obtained for well-known families of distributions. We have also considered applications of the distance to parameter estimation, testing statistical hypotheses and discriminant analysis.

Stability of invariant linearly sufficient statistics in the general Gauss-Markov model

Andrzej Kornacki (1997)

Applications of Mathematics

Necessary and sufficient conditions are derived for the inclusions J 0 ⊂ J and J 0 * ⊂ J * to be fulfilled where J 0 , J 0 * and J , J * are some classes of invariant linearly sufficient statistics (Oktaba, Kornacki, Wawrzosek (1988)) corresponding to the Gauss-Markov models G M 0 = ( y , X 0 β 0 , σ 0 2 V 0 ) and G M = ( y , X β , σ 2 V ) , respectively.

Stability of stochastic optimization problems - nonmeasurable case

Petr Lachout (2008)

Kybernetika

This paper deals with stability of stochastic optimization problems in a general setting. Objective function is defined on a metric space and depends on a probability measure which is unknown, but, estimated from empirical observations. We try to derive stability results without precise knowledge of problem structure and without measurability assumption. Moreover, ε -optimal solutions are considered. The setup is illustrated on consistency of a ε - M -estimator in linear regression model.

Stable hypothesis for mixed models with balanced cross-nesting

Dário Ferreira, João Tiago Mexia, Sandra Saraiva Ferreira (2005)

Discussiones Mathematicae Probability and Statistics

Stable hypothesis are hypothesis that may refer either for the fixed part or for the random part of the model. We will consider such hypothesis for models with balanced cross-nesting. Generalized F tests will be derived. It will be shown how to use Monte-Carlo methods to evaluate p-values for those tests.

Stable-1/2 bridges and insurance

Edward Hoyle, Lane P. Hughston, Andrea Macrina (2015)

Banach Center Publications

We develop a class of non-life reserving models using a stable-1/2 random bridge to simulate the accumulation of paid claims, allowing for an essentially arbitrary choice of a priori distribution for the ultimate loss. Taking an information-based approach to the reserving problem, we derive the process of the conditional distribution of the ultimate loss. The "best-estimate ultimate loss process" is given by the conditional expectation of the ultimate loss. We derive explicit expressions for the...

Stacked regression with restrictions

Tomasz Górecki (2005)

Discussiones Mathematicae Probability and Statistics

When we apply stacked regression to classification we need only discriminant indices which can be negative. In many situations, we want these indices to be positive, e.g., if we want to use them to count posterior probabilities, when we want to use stacked regression to combining classification. In such situation, we have to use leastsquares regression under the constraint βₖ ≥ 0, k = 1,2,...,K. In their earlier work [5], LeBlanc and Tibshirani used an algorithm given in [4]. However, in this paper...

Currently displaying 41 – 60 of 94