Local robustness of sign tests in AR(1) against outliers

Boldin, M. V.

doi:10.3103/s1066530711010017

Cited by 10 publications

(10 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…In this paper we will not discuss experimental facts received, for example, by statistical modeling.) The only exception is [6], where local qualitative robustness of sign test in the AR (1) in the data contamination scheme by independent additive outliers is estimated. This qualitative robustness is formulated in terms of equicontinuity of the level and power of the test against the level of contamination.…”

Section: Introduction and Statement Of The Problemmentioning

confidence: 99%

Robustness of Sign Tests for Testing Hypotheses about Order of Autoregression

Boldin¹

2013

Theory Probab. Appl.

Self Cite

View full text Add to dashboard Cite

Observations of autoregression are contaminated by additive isolated outliers with an unknown random distribution. Intensity of the outliers γn is min(1, n −1/2 γ), where γ 0 is unknown, and n is the data size. Robustness of sign tests for hypotheses about order of autoregression is considered. The result is formulated in terms of equicontinuity of limiting power with respect to γ at γ = 0.

show abstract

Section: Introduction and Statement Of The Problemmentioning

confidence: 99%

Robustness of Sign Tests for Testing Hypotheses about Order of Autoregression

Boldin¹

2013

Theory Probab. Appl.

Self Cite

View full text Add to dashboard Cite

show abstract

“…Исключением является работа [6], в которой установлена локальная качественная ро-бастность знакового теста в AR(1) в схеме засорения данных независимыми аддитив-ными выбросами. Эта качественная робастность формулируется в терминах равно-степенной непрерывности уровня и мощности теста относительно уровня засорения.…”

unclassified

“…Цель настоящей работы -в схеме засорения данных из [6] установить робаст-ность знаковых тестов для гипотез о порядке авторегрессии.…”

unclassified

“…Отличная от (1.3) характеризация робастности при конечных n (в духе [6]) упо-мянута в замечании 2.5.…”

unclassified

“…Качественная робастность знакового теста в схеме (1.2) может быть сформулирована и для конечных n. А именно, пусть Wn(τ ) := W n (τ , 0, μ) -мощность при конечных n в схеме без засорения. Можно показать, что знаковый тест удовлетворяет условию локальной качественной робастности (опреде-ление введено в [6]), т.е.…”

unclassified

See 2 more Smart Citations

Робастность Знаковых Тестов Для Гипотез О Порядке Авторегрессии

Boldin¹

2012

Teoriya Veroyatnostei i ee Primeneniya

View full text Add to dashboard Cite

Tests Based on Simplicial Depth for AR(1) Models With Explosion

Kustosz

Leucht

Müller

2016

Journal Time Series Analysis

View full text Add to dashboard Cite

We propose an outlier robust and distributions-free test for the explosive AR(1) model with intercept based on simplicial depth. In this model, simplicial depth reduces to counting the cases where three residuals have alternating signs. Using this, it is shown that the asymptotic distribution of the test statistic is given by an integrated two-dimensional Gaussian process. Conditions for the consistency of the test are given and the power of the test at finite samples is compared with five alternative tests, using errors with normal distribution, contaminated normal distribution, and Fréchet distribution in a simulation study. The comparisons show that the new test outperforms all other tests in the case of skewed errors and outliers. Although here we deal with the AR(1) model with intercept only, the asymptotic results hold for any simplicial depth which reduces to alternating signs of three residuals.

show abstract

Local robustness of sign tests in AR(1) against outliers

Cited by 10 publications

References 16 publications

Robustness of Sign Tests for Testing Hypotheses about Order of Autoregression

Robustness of Sign Tests for Testing Hypotheses about Order of Autoregression

Робастность Знаковых Тестов Для Гипотез О Порядке Авторегрессии

Tests Based on Simplicial Depth for AR(1) Models With Explosion

Contact Info

Product

Resources

About