Testing for parameter changes in linear state space models

Golosnoy, Vasyl; Köhler, Steffen; Schmid, Wolfgang; Seifert, Miriam Isabel

doi:10.1002/asmb.2636

Cited by 3 publications

(3 citation statements)

References 51 publications

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“…For this purpose we elaborate both CUSUM and EWMA control charts for online detection of changes from AR(1) to ARMA(1,1) and vice versa. The control statistic for these charts is based on the testing approach elaborated by Golosnoy et al ( 2021 ). In the Monte Carlo simulation study we show that these changes (which are numerically rather small compared to the process variance) could be detected not immediately but only with some detection delay.…”

Section: Discussionmentioning

confidence: 99%

“…As we observe in ( 7 ), the MA(1) parameter influences linearly the covariance . For this reason we introduce another variable defined as Next, by making use of the ideas and the results in Golosnoy et al ( 2021 ) we derive the moments of which is given in the following proposition.…”

Section: The Measurement Error Modelmentioning

confidence: 99%

“…In particular, we consider both exponentially weighted moving average (EWMA) and cumulative sum (CUSUM) control charts which are the most popular monitoring procedures in practice. The control statistics suitable for our monitoring task are derived based on the ideas of the statistical testing approach recently developed by Golosnoy et al ( 2021 ) for the class of linear state space models. We investigate the detecting ability of these control charts for MA(1) parameter changes in an extensive Monte Carlo simulation study.…”

Section: Introductionmentioning

confidence: 99%

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Control charts for measurement error models

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We consider a linear measurement error model (MEM) with AR(1) process in the state equation which is widely used in applied research. This MEM could be equivalently re-written as ARMA(1,1) process, where the MA(1) parameter is related to the variance of measurement errors. As the MA(1) parameter is of essential importance for these linear MEMs, it is of much relevance to provide instruments for online monitoring in order to detect its possible changes. In this paper we develop control charts for online detection of such changes, i.e., from AR(1) to ARMA(1,1) and vice versa, as soon as they occur. For this purpose, we elaborate on both cumulative sum (CUSUM) and exponentially weighted moving average (EWMA) control charts and investigate their performance in a Monte Carlo simulation study. The empirical illustration of our approach is conducted based on time series of daily realized volatilities.

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Section: Discussionmentioning

confidence: 99%

Section: The Measurement Error Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Control charts for measurement error models

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The effect of intraday periodicity on realized volatility measures

Dette

Golosnoy

Kellermann

2022

Metrika

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We focus on estimating daily integrated volatility (IV) by realized measures based on intraday returns following a discrete-time stochastic model with a pronounced intraday periodicity (IP). We demonstrate that neglecting the IP-impact on realized estimators may lead to invalid statistical inference concerning IV for a common finite number of intraday returns. For a given IP functional form, we analytically derive robust IP-correction factors for realized measures of IV as well as their asymptotic distributions. We show both in Monte Carlo simulations and empirically that the proposed bias corrections are the robust way to account for IP by computing realized estimators.

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A control chart for monitoring image processes based on convolutional neural networks

Okhrin,

Schmid,

Semeniuk

2024

Statistica Neerlandica

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In this paper, the problem of monitoring image processes with spatially correlated pixels over time is considered. An exponentially weighted moving average (EWMA) control chart for monitoring such processes based on a convolutional neural network (CNN) is proposed. A comparison of its performance with a Hotelling's control chart and with a control chart based on generalized likelihood ratio (GLR) approach is conducted through a simulation study. The new method outperforms other methods in most of the cases considered in the simulation study. A technique for mean intensity shift localization based on CNNs is proposed and evaluated.

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Testing for parameter changes in linear state space models

Cited by 3 publications

References 51 publications

Control charts for measurement error models

Control charts for measurement error models

The effect of intraday periodicity on realized volatility measures

A control chart for monitoring image processes based on convolutional neural networks

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