Marie Hušková
Charles University in Prague
Network
Latest external collaboration on country level. Dive into details by clicking on the dots.
Publication
Featured researches published by Marie Hušková.
Econometrics Journal | 2006
Alexander Aue; Lajos Horváth; Marie Hušková; Piotr Kokoszka
, both methods have correct asymptotic size and detect a change with probability approaching unity. The methods are illustrated and compared in a small simulation study. Copyright Royal Economic Society 2006
Journal of Statistical Planning and Inference | 1997
Jaromír Antoch; Marie Hušková; Zuzana Prášková
Abstract Quite a number of test statistics and estimators for detection of a change in the mean of a series of independent observations were proposed and studied. The purpose of this paper is to examine the behaviour of these statistics if the observations are dependent, particularly, if they form a linear process.
Journal of Time Series Analysis | 2012
Lajos Horváth; Marie Hušková
We consider N panels and each panel is based on T observations. We are interested to test if the means of the panels remain the same during the observation period against the alternative that the means change at an unknown time. We provide tests which are derived from a likelihood argument and they are based on the adaptation of the CUSUM method to panel data. Asymptotic distributions are derived under the no change null hypothesis and the consistency of the tests are proven under the alternative. The asymptotic results are shown to work in case of small and moderate sample sizes via Monte Carlo simulations.
Journal of Nonparametric Statistics | 1995
Jaromír Antoch; Marie Hušková; Noël Veraverbeke
We consider a class of simple estimators of the change-point m in a sequence of n independent random variables X1…,X n satisfying E(X i ) = θ0 for i = 1,…,m and E(X i ) = θ0+δ n for i = m +1,…n. (θ0 and δ n are unknown). We obtain rates of consistency for the estimator, derive its limiting distribution and show that the bootstrap approximation is asymptotically valid. The results are illustrated by some simulations.
Probability Theory and Related Fields | 1970
Marie Hušková
SummaryThe object of the present paper is to derive asymptotic distributions of simple linear rank statistics used for testing symmetry under various systems of conditions. The main result is stated in Theorem 3 (Section 2) and in Theorem 6 (Section 3). The assertions have been derived by the method worked out in [1]. Similar assertions concerning the multivariate case (the other part of the authors thesis) will be published in a next paper.
Econometric Theory | 2012
Alexander Aue; Siegfried Hörmann; Lajos Horváth; Marie Hušková; Josef Steinebach
Despite substantial criticism, variants of the capital asset pricing model (CAPM) remain to this day the primary statistical tools for portfolio managers to assess the performance of financial assets. In the CAPM, the risk of an asset is expressed through its correlation with the market, widely known as the beta. There is now a general consensus among economists that these portfolio betas are time-varying and that, consequently, any appropriate analysis has to take this variability into account. Recent advances in data acquisition and processing techniques have led to an increased research output concerning high-frequency models. Within this framework, we introduce here a modified functional CAPM and sequential monitoring procedures to test for the constancy of the portfolio betas. As our main results we derive the large-sample properties of these monitoring procedures. In a simulation study and an application to S&P 100 data we show that our method performs well in finite samples.
Statistics & Probability Letters | 1997
Marie Hušková
The purpose of the paper is to extend the weak asymptotic results for the weighted partial sums of i.i.d. random variables to the weighted partial sums of rank scores. These results then suggest various test procedures for the change point problem. The crucial tools in the proofs are martingale property of a class of two-sample rank statistics and the Hajek results (1961) of the simple linear rank statistics.
Journal of Time Series Analysis | 2008
Marie Hušková; Claudia Kirch
We study an at-most-one-change time-series model with an abrupt change in the mean and dependent errors that fulfil certain mixing conditions. We obtain confidence intervals for the unknown change-point via bootstrapping methods. Precisely, we use a block bootstrap of the estimated centred error sequence. Then, we reconstruct a sequence with a change in the mean using the same estimators as before. The difference between the change-point estimator of the resampled sequence and the one of the original sequence can be used as an approximation of the difference between the real change-point and its estimator. This enables us to construct confidence intervals using the empirical distribution of the resampled time series. A simulation study shows that the resampled confidence intervals are usually closer to their target levels and at the same time smaller than the asymptotic intervals. Copyright 2008 The Authors. Journal compilation 2008 Blackwell Publishing Ltd
Journal of Statistical Planning and Inference | 1999
Marie Hušková
Abstract A number of papers have been published on the estimation problem in location models with abrupt changes (e.g., Csorgő and Horvath, 1997 ). In the present paper we focus on estimators in location models with various gradual changes. Least type squares estimators of the parameters are proposed and studied. It appears that the limit behavior (both the rate of consistency and limit distribution) of the estimators of the change point in location models depends on how the type of gradual changes differ.
Journal of Nonparametric Statistics | 2008
Marie Hušková; Simos G. Meintanis
Tests for the multivariate k-sample problem are considered. The tests are based on the weighted L2 distance between empirical characteristic functions, and afford an interesting interpretation in terms of a corresponding test statistic based on the L2 distance of pairs of non-parametric density estimators. Depending on the choice of weighting, a corresponding Dirac-type weight function reduces the test to a normalised version of the L2 distance between the sample means of the k populations. Theoretical and computational issues are considered, while the finite-sample implementation based on the permutation distribution of the test statistic shows that the new test performs well in comparison with alternative procedures of the change-point type.