Robustness of estimation of first-order autoregressive model under contaminated uniform white noise

Karima Nouali

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Displaying similar documents to “Robustness of estimation of first-order autoregressive model under contaminated uniform white noise”

Approximate bias for first-order autoregressive model with uniform innovations. Small sample case

Karima Nouali, Hocine Fellag (2002)

Discussiones Mathematicae Probability and Statistics

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The first-order autoregressive model with uniform innovations is considered. The approximate bias of the maximum likelihood estimator (MLE) of the parameter is obtained. Also, a formula for the approximate bias is given when a single outlier occurs at a specified time with a known amplitude. Simulation procedures confirm that our formulas are suitable. A small sample case is considered only.

Bayesian estimation of AR(1) models with uniform innovations

Hocine Fellag, Karima Nouali (2005)

Discussiones Mathematicae Probability and Statistics

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The first-order autoregressive model with uniform innovations is considered. In this paper, we propose a family of BAYES estimators based on a class of prior distributions. We obtain estimators of the parameter which perform better than the maximum likelihood estimator.

$A R (1)$ Time Series with Approximated Beta Marginal

Božidar V. Popović (2010)

Publications de l'Institut Mathématique

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Univariate parametric survival analysis using GS-distributions.

Albert Sorribas, José M. Muiño, Montserrat Rué, Joan Fibla (2006)

SORT

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The GS-distribution is a family of distributions that provide an accurate representation of any unimodal univariate continuous distribution. In this contribution we explore the utility of this family as a general model in survival analysis. We show that the survival function based on the GS-distribution is able to provide a model for univariate survival data and that appropriate estimates can be obtained. We develop some hypotheses tests that can be used for checking the underlying survival...

Application of MCMC to change point detection

Jaromír Antoch, David Legát (2008)

Applications of Mathematics

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A nonstandard approach to change point estimation is presented in this paper. Three models with random coefficients and Bayesian approach are used for modelling the year average temperatures measured in Prague Klementinum. The posterior distribution of the change point and other parameters are estimated from the random samples generated by the combination of the Metropolis-Hastings algorithm and the Gibbs sampler.

AR models with uniformly distributed noise

Michal Horváth (1989)

Aplikace matematiky

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AR models are frequently used but usually with normally distributed white noise. In this paper AR model with uniformly distributed white noise are introduces. The maximum likelihood estimation of unknown parameters is treated, iterative method for the calculation of estimates is presented. A numerical example of this procedure and simulation results are also given.

New generalization of compound Rayleigh distribution: Different estimation methods based on progressive type-II censoring schemes and applications

Omid Shojaee, Reza Azimi (2025)

Applications of Mathematics

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Fitting a suitable distribution to the data from a real experiment is a crucial topic in statistics. However, many of the existing distributions cannot account for the effect of environmental conditions on the components under test. Moreover, the components are usually heterogeneous, meaning that they do not share the same distribution. In this article, we aim to obtain a new generalization of the Compound Rayleigh distribution by using mixture models and incorporating the environmental...

A new uniform Ar(1) time series model (nuar(1))

Miroslav M. Ristić, Biljana Č. Popović (2000)

Publications de l'Institut Mathématique

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Variance function estimation via model selection

Teresa Ledwina, Jan Mielniczuk (2010)

Applicationes Mathematicae

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The problem of estimating an unknown variance function in a random design Gaussian heteroscedastic regression model is considered. Both the regression function and the logarithm of the variance function are modelled by piecewise polynomials. A finite collection of such parametric models based on a family of partitions of support of an explanatory variable is studied. Penalized model selection criteria as well as post-model-selection estimates are introduced based on Maximum Likelihood...

Sampling inference, Bayes' inference and robustness in the advancement of learning.

George E. P. Box (1980)

Trabajos de Estadística e Investigación Operativa

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Scientific learning is seen as an iterative process employing Criticism and Estimation. Sampling theory use of predictive distributions for model criticism is examined and also the implications for significance tests and the theory of precise measurement. Normal theory examples and ridge estimates are considered. Predictive checking functions for transformation, serial correlation, and bad values are reviewed as is their relation with Bayesian options. Robustness is seen from a Bayesian...