Displaying similar documents to “Testing Linearity in an AR Errors-in-variables Model with Application to Stochastic Volatility”

Behaviour of higher-order approximations of the tests in the single parameter Cox proportional hazards model

Aneta Andrášiková, Eva Fišerová (2020)

Applications of Mathematics

Similarity:

Survival analysis is applied in a wide range of sectors (medicine, economy, etc.), and its main idea is based on evaluating the time until the occurrence of an event of interest. The effect of some particular covariates on survival time is usually described by the Cox proportional hazards model and the statistical significance of the impact of covariates is verified by the likelihood ratio test, the Wald test, or the score test. In addition to standard tests, appropriate higher-order...

On a robust significance test for the Cox regression model

Tadeusz Bednarski, Filip Borowicz (2006)

Discussiones Mathematicae Probability and Statistics

Similarity:

A robust significance testing method for the Cox regression model, based on a modified Wald test statistic, is discussed. Using Monte Carlo experiments the asymptotic behavior of the modified robust versions of the Wald statistic is compared with the standard significance test for the Cox model based on the log likelihood ratio test statistic.

Models for stochastic mortality

Jan Iwanik (2007)

Applicationes Mathematicae

Similarity:

This paper is an attempt to present and analyse stochastic mortality models. We propose a couple of continuous-time stochastic models that are natural generalizations of the Gompertz law in the sense that they reduce to the Gompertz function when the volatility parameter is zero. We provide a statistical analysis of the available demographic data to show that the models fit historical data well. Finally, we give some practical examples for the multidimensional models.

Selective F tests for sub-normal models

Célia Maria Pinto Nunes, João Tiago Mexia (2003)

Discussiones Mathematicae Probability and Statistics

Similarity:

F tests that are specially powerful for selected alternatives are built for sub-normal models. In these models the observation vector is the sum of a vector that stands for what is measured with a normal error vector, both vectors being independent. The results now presented generalize the treatment given by Dias (1994) for normal fixed-effects models, and consider the testing of hypothesis on the ordering of mean values and components.

Checking proportional rates in the two-sample transformation model

David Kraus (2009)

Kybernetika

Similarity:

Transformation models for two samples of censored data are considered. Main examples are the proportional hazards and proportional odds model. The key assumption of these models is that the ratio of transformation rates (e. g., hazard rates or odds rates) is constant in time. A~method of verification of this proportionality assumption is developed. The proposed procedure is based on the idea of Neyman's smooth test and its data-driven version. The method is suitable for detecting monotonic...

Unit root test under innovation outlier contamination small sample case

Lynda Atil, Hocine Fellag, Karima Nouali (2006)

Discussiones Mathematicae Probability and Statistics

Similarity:

The two sided unit root test of a first-order autoregressive model in the presence of an innovation outlier is considered. In this paper, we present three tests; two are usual and one is new. We give formulas computing the size and the power of the three tests when an innovation outlier (IO) occurs at a specified time, say k. Using a comparative study, we show that the new statistic performs better under contamination. A Small sample case is considered only.