BICKEL–ROSENBLATT TEST FOR WEAKLY DEPENDENT DATA

The aim of this paper is to analyze the Bickel–Rosenblatt test for simple hypothesis in case of weakly dependent data. Although the test has nice theoretical properties, it is not clear how to implement it in practice. Choosing different band-width sequences first we analyze percentage rejections of the test statistic under H0 by some empirical simulation analysis. This can serve as an approximate rule for choosing the bandwidth in case of simple hypothesis for practical implementation of the test. In the recent paper [12] a version of Neyman goodness-of-fit test was established for weakly dependent data in the case of simple hypotheses. In this paper we also aim to compare and discuss the applicability of these tests for both independent and dependent observations.

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Adaptive estimation of an additive regression function from weakly dependent data

Journal of Multivariate Analysis ◽

10.1016/j.jmva.2014.09.005 ◽

2015 ◽

Vol 133 ◽

pp. 77-94 ◽

Cited By ~ 5

Author(s):

Christophe Chesneau ◽

Jalal Fadili ◽

Bertrand Maillot

Keyword(s):

Adaptive Estimation ◽

Regression Function ◽

Dependent Data ◽

Additive Regression ◽

Weakly Dependent Data ◽

Weakly Dependent

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Research on distributions of chi-squared goodness-of-fit test statistic for parametric generalized proportional hazards models

2014 12th International Conference on Actual Problems of Electronics Instrument Engineering (APEIE) ◽

10.1109/apeie.2014.7040747 ◽

2014 ◽

Author(s):

M.A. Semenova ◽

E.V. Chimitova

Keyword(s):

Goodness Of Fit ◽

Proportional Hazards ◽

Test Statistic ◽

Goodness Of Fit Test ◽

Proportional Hazards Models ◽

Chi Squared

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A test for fuzzy exponentiality based on Kullback-Leibler information

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-202555 ◽

2021 ◽

pp. 1-8

Author(s):

Lingtao Kong

Keyword(s):

Biological Sciences ◽

Monte Carlo ◽

Goodness Of Fit ◽

Experimental Studies ◽

Real Data ◽

Test Statistics ◽

Test Statistic ◽

Goodness Of Fit Test ◽

Higher Power ◽

Leibler Information

The exponential distribution has been widely used in engineering, social and biological sciences. In this paper, we propose a new goodness-of-fit test for fuzzy exponentiality using α-pessimistic value. The test statistics is established based on Kullback-Leibler information. By using Monte Carlo method, we obtain the empirical critical points of the test statistic at four different significant levels. To evaluate the performance of the proposed test, we compare it with four commonly used tests through some simulations. Experimental studies show that the proposed test has higher power than other tests in most cases. In particular, for the uniform and linear failure rate alternatives, our method has the best performance. A real data example is investigated to show the application of our test.

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Testing in Nonparametric Accelerated Life Time Models

Austrian Journal of Statistics ◽

10.17713/ajs.v37i1.289 ◽

2016 ◽

Vol 37 (1) ◽

Cited By ~ 1

Author(s):

Hannelore Liero

Keyword(s):

Limit Distribution ◽

Goodness Of Fit ◽

Bootstrap Method ◽

Life Time ◽

Test Statistic ◽

Goodness Of Fit Test ◽

Time Model ◽

Power Of The Test ◽

Accelerated Life ◽

Type Test

A goodness-of-fit test for testing the acceleration function in a nonparametric life time model is proposed. For this aim the limit distribution of an L2-type test statistic is derived. Furthermore, a bootstrap method is considered and the power of the test is studied.

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Tracking Pandemic Severity Using Data on the Age Structure of Mortality: Lessons From the 1918 Influenza Pandemic in Michigan

American Journal of Public Health ◽

10.2105/ajph.2021.306303 ◽

2021 ◽

Vol 111 (S2) ◽

pp. S149-S155

Author(s):

Siddharth Chandra ◽

Julia Christensen

Keyword(s):

Public Health ◽

Age Structure ◽

Goodness Of Fit ◽

Influenza Pandemic ◽

Age Distribution ◽

Test Statistic ◽

Goodness Of Fit Test ◽

1918 Influenza ◽

Death Data ◽

Excess Deaths

Objectives. To test whether distortions in the age structure of mortality during the 1918 influenza pandemic in Michigan tracked the severity of the pandemic. Methods. We calculated monthly excess deaths during the period of 1918 to 1920 by using monthly data on all-cause deaths for the period of 1912 to 1920 in Michigan. Next, we measured distortions in the age distribution of deaths by using the Kuiper goodness-of-fit test statistic comparing the monthly distribution of deaths by age in 1918 to 1920 with the baseline distribution for the corresponding month for 1912 to 1917. Results. Monthly distortions in the age distribution of deaths were correlated with excess deaths for the period of 1918 to 1920 in Michigan (r = 0.83; P < .001). Conclusions. Distortions in the age distribution of deaths tracked variations in the severity of the 1918 influenza pandemic. Public Health Implications. It may be possible to track the severity of pandemic activity with age-at-death data by identifying distortions in the age distribution of deaths. Public health authorities should explore the application of this approach to tracking the COVID-19 pandemic in the absence of complete data coverage or accurate cause-of-death data.

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