Local influence on the goodness-of-fit test statistic in maximum likelihood factor analysis

1998 ◽  
Vol 5 (2) ◽  
pp. 447-455 ◽  
Author(s):  
Kang -Mo Jung
Keyword(s):  
Factor Analysis ◽  
Maximum Likelihood ◽  
Goodness Of Fit ◽  
Local Influence ◽  
Test Statistic ◽  
Goodness Of Fit Test ◽  
Maximum Likelihood Factor Analysis

Maximum Likelihood Factor Analysis of Attitude Data

1977 ◽  
Vol 14 (1) ◽  
pp. 42-51 ◽  
Author(s):  
Roger M. Heeler ◽  
Thomas W. Whipple ◽  
Thomas P. Hustad
Keyword(s):  
Factor Analysis ◽  
Maximum Likelihood ◽  
Goodness Of Fit ◽  
Factor Solution ◽  
Maximum Likelihood Factor Analysis

Maximum likelihood factor analysis is a useful technique for analyzing attitude data. The solution can be tested statistically for goodness of fit. Companion procedures for restricting the factor solution permit the testing of hypothesized factor structures. Thus the technique can be used to construct solutions that are more clearly interpretable while still providing adequate fit to the data. A case example is given to illustrate the use of the technique.

scholarly journals Goodness-of-fit Testing for Left-truncated Two-parameter Weibull Distributions with Known Truncation Point

2016 ◽  
Vol 45 (3) ◽  
pp. 15-42 ◽  
Author(s):  
Ayse KIZILERSU ◽  
Markus Kreer ◽  
Anthony W. Thomas
Keyword(s):  
Maximum Likelihood ◽  
Weibull Distribution ◽  
Goodness Of Fit ◽  
Brownian Bridge ◽  
Critical Values ◽  
Test Statistic ◽  
Goodness Of Fit Test ◽  
Weibull Parameters ◽  
Kolmogorov Smirnov ◽  
Power Testing

The left-truncated Weibull distribution is used in life-time analysis, it has many applications ranging from financial market analysis and insurance claims to the earthquake inter-arrival times.We present a comprehensive analysis of the left-truncated Weibull distribution when the shape, scale or both parameters are unknown and they are determined from the data using the maximum likelihood estimator. We demonstrate that if both the Weibull parameters are unknown then there are sets of sample configurations, with measure greater than zero, for which the maximum likelihood equations do not possess non trivial solutions. The modified critical values of the goodness-of-fit test from the Kolmogorov-Smirnov test statistic when the parameters are unknown are obtained from a quantile analysis. We find that the critical values depend on sample size and truncation level, but  not on the actual Weibull parameters.  Confirming this behavior, we present a complementary analysis using the Brownian bridge approach as an asymptotic limit of the Kolmogorov-Smirnov statistics and find that both approaches are in good agreement. A power testing is performed for our Kolmogorov-Smirnov goodness-of-fit test and  the issues related to the left-truncated data are discussed. We conclude the paper by showing the importance of left-truncated Weibulldistribution hypothesis testing on  the duration times of failed marriages in the US, worldwide terrorist attacks, waiting times between stock market orders, and time intervals of radioactive decay.

Local influence in maximum likelihood factor analysis

1997 ◽  
Vol 24 (4) ◽  
pp. 483-491 ◽  
Author(s):  
Kang-Mo Jung ◽  
Myung Geun Kim ◽  
Byung Chun Kim
Keyword(s):  
Factor Analysis ◽  
Maximum Likelihood ◽  
Local Influence ◽  
Maximum Likelihood Factor Analysis

scholarly journals A powerful goodness-of-fit test for Lindley distribution with application to real data

2021 ◽  
pp. 761-769
Author(s):  
Hadi Alizadeh Noughabi
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Maximum Likelihood ◽  
Critical Points ◽  
Goodness Of Fit ◽  
Real Data ◽  
Reliability Model ◽  
Test Statistic ◽  
Goodness Of Fit Test ◽  
Lindley Distribution

The Lindley distribution may serve as a useful reliability model. Applications of this distribution are presented in statistical literature. In this article, a powerful goodness of fit test for the Lindley distribution is proposed. In order to compute the proposed test statistic, we use the maximum likelihood estimate (MLE) suggested by Ghitany et al. (2008), which is simple explicit estimator. By Monte Carlo simulation, critical points of the proposed test statistic for different sample sizes are obtained. Power values of the proposed test are compared with the competing tests against various alternatives via simulations. Finally, two real data are presented and analyzed.

A test for fuzzy exponentiality based on Kullback-Leibler information

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.

scholarly journals Testing in Nonparametric Accelerated Life Time Models

2016 ◽  
Vol 37 (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.

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

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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