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Global robust output regulation of a class of nonlinear systems with nonlinear exosystems

Yuan Jiang, Ke Lu, Jiyang Dai (2020)

Kybernetika

An adaptive output regulation design method is proposed for a class of output feedback systems with nonlinear exosystem and unknown parameters. A new nonlinear internal model approach is developed in the present study that successfully converts the global robust output regulation problem into a robust adaptive stabilization problem for the augmented system. Moreover, an output feedback controller is achieved based on a type of state filter which is designed for the transformed augmented system....

Global statistical information in exponential experiments and selection of exponential models

Igor Vajda, E. van der Meulen (1998)

Applications of Mathematics

The concept of global statistical information in the classical statistical experiment with independent exponentially distributed samples is investigated. Explicit formulas are evaluated for common exponential families. It is shown that the generalized likelihood ratio test procedure of model selection can be replaced by a generalized information procedure. Simulations in a classical regression model are used to compare this procedure with that based on the Akaike criterion.

Goodness-of-fit test for long range dependent processes

Gilles Fay, Anne Philippe (2002)

ESAIM: Probability and Statistics

In this paper, we make use of the information measure introduced by Mokkadem (1997) for building a goodness-of-fit test for long-range dependent processes. Our test statistic is performed in the frequency domain and writes as a non linear functional of the normalized periodogram. We establish the asymptotic distribution of our statistic under the null hypothesis. Under specific alternative hypotheses, we prove that the power converges to one. The performance of our test procedure is illustrated...

Goodness-of-fit test for long range dependent processes

Gilles Fay, Anne Philippe (2010)

ESAIM: Probability and Statistics

In this paper, we make use of the information measure introduced by Mokkadem (1997) for building a goodness-of-fit test for long-range dependent processes. Our test statistic is performed in the frequency domain and writes as a non linear functional of the normalized periodogram. We establish the asymptotic distribution of our statistic under the null hypothesis. Under specific alternative hypotheses, we prove that the power converges to one. The performance of our test procedure is illustrated...

Goodness-of-fit test for the family of logistic distributions.

N. Aguirre, Mikhail S. Nikulin (1994)

Qüestiió

Chi-squared goodness-of-fit test for the family of logistic distributions id proposed. Different methods of estimation of the unknown parameters θ of the family are compared. The problem of homogeneity is considered.

Goodness-of-fit tests based on K φ -divergence

Teresa Pérez, Julio A. Pardo (2003)

Kybernetika

In this paper a new family of statistics based on K φ -divergence for testing goodness-of-fit under composite null hypotheses are considered. The asymptotic distribution of this test is obtained when the unspecified parameters are estimated by maximum likelihood as well as minimum K φ -divergence.

Goodness-of-fit tests for parametric regression models based on empirical characteristic functions

Marie Hušková, Simon G. Meintanis (2009)

Kybernetika

Test procedures are constructed for testing the goodness-of-fit in parametric regression models. The test statistic is in the form of an L2 distance between the empirical characteristic function of the residuals in a parametric regression fit and the corresponding empirical characteristic function of the residuals in a non-parametric regression fit. The asymptotic null distribution as well as the behavior of the test statistic under contiguous alternatives is investigated. Theoretical results are...

Goodness-of-fit tests in long-range dependent processes under fixed alternatives

Holger Dette, Kemal Sen (2013)

ESAIM: Probability and Statistics

In a recent paper Fay and Philippe [ESAIM: PS 6 (2002) 239–258] proposed a goodness-of-fit test for long-range dependent processes which uses the logarithmic contrast as information measure. These authors established asymptotic normality under the null hypothesis and local alternatives. In the present note we extend these results and show that the corresponding test statistic is also normally distributed under fixed alternatives.

How powerful are data driven score tests for uniformity

Tadeusz Inglot, Alicja Janic (2009)

Applicationes Mathematicae

We construct a new class of data driven tests for uniformity, which have greater average power than existing ones for finite samples. Using a simulation study, we show that these tests as well as some "optimal maximum test" attain an average power close to the optimal Bayes test. Finally, we prove that, in the middle range of the power function, the loss in average power of the "optimal maximum test" with respect to the Neyman-Pearson tests, constructed separately for each alternative, in the Gaussian...

How the initialization affects the stability of the қ-means algorithm

Sébastien Bubeck, Marina Meilă, Ulrike von Luxburg (2012)

ESAIM: Probability and Statistics

We investigate the role of the initialization for the stability of the қ-means clustering algorithm. As opposed to other papers, we consider the actual қ-means algorithm (also known as Lloyd algorithm). In particular we leverage on the property that this algorithm can get stuck in local optima of the қ-means objective function. We are interested in the actual clustering, not only in the costs of the solution. We analyze when different initializations lead to the same local optimum, and when they...

How the initialization affects the stability of the қ-means algorithm∗

Sébastien Bubeck, Marina Meilă, Ulrike von Luxburg (2012)

ESAIM: Probability and Statistics

We investigate the role of the initialization for the stability of the қ-means clustering algorithm. As opposed to other papers, we consider the actual қ-means algorithm (also known as Lloyd algorithm). In particular we leverage on the property that this algorithm can get stuck in local optima of the қ-means objective function. We are interested in the actual clustering, not only in the costs of the solution. We analyze when different initializations...

Hypotheses testing with the two-parameter Pareto distribution on the basis of records in fuzzy environment

Ali Reza Saeidi, Mohammad Ghasem Akbari, Mahdi Doostparast (2014)

Kybernetika

In problems of testing statistical hypotheses, we may be confronted with fuzzy concepts. There are also situations in which the available data are record statistics such as weather and sports. In this paper, we consider the problem of testing fuzzy hypotheses on the basis of records. Pareto distribution is investigated in more details since it is used in applications including economic and life testing analysis. For illustrative proposes, a real data set on annual wage is analyzed using the results...

Hypothesis testing in unbalanced two-fold nested random models

Marcin Przystalski (2016)

Applicationes Mathematicae

In many applications of linear random models to multilevel data, it is of interest to test whether the random effects variance components are zero. In this paper we propose approximate tests for testing significance of variance components in the unbalanced two-fold nested random model in the presence of non-normality. In the derivations of the asymptotic distributions of the test statistics, as an intermediate result, the explicit form of the asymptotic covariance matrix of the vector of mean squares...

Currently displaying 501 – 520 of 1241