Matching moments for a closer approximation of the weighted -divergence test statistics in goodness-of-fit for finite samples

2005 ◽  
Vol 342 (1) ◽  
pp. 115-129 ◽  
Author(s):  
E. Landaburu ◽  
L. Pardo
Keyword(s):  
Goodness Of Fit ◽  
Test Statistics ◽  
Finite Samples ◽  
Matching Moments

scholarly journals Goodness-of-Fit Tests for Bivariate Time Series of Counts

Econometrics ◽  
2021 ◽  
Vol 9 (1) ◽  
pp. 10
Author(s):  
Šárka Hudecová ◽  
Marie Hušková ◽  
Simos G. Meintanis
Keyword(s):  
Goodness Of Fit ◽  
Probability Generating Function ◽  
Parametric Bootstrap ◽  
Real Data ◽  
Data Sets ◽  
Test Statistics ◽  
Finite Sample ◽  
Generalized Poisson ◽  
Goodness Of Fit Tests ◽  
Monte Carlo Experiments

This article considers goodness-of-fit tests for bivariate INAR and bivariate Poisson autoregression models. The test statistics are based on an L2-type distance between two estimators of the probability generating function of the observations: one being entirely nonparametric and the second one being semiparametric computed under the corresponding null hypothesis. The asymptotic distribution of the proposed tests statistics both under the null hypotheses as well as under alternatives is derived and consistency is proved. The case of testing bivariate generalized Poisson autoregression and extension of the methods to dimension higher than two are also discussed. The finite-sample performance of a parametric bootstrap version of the tests is illustrated via a series of Monte Carlo experiments. The article concludes with applications on real data sets and discussion.

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.

Testing Spatial Dependence in Spatial Models with Endogenous Weights Matrices

2018 ◽  
Vol 8 (1) ◽  
Author(s):  
Anil K. Bera ◽  
Osman Doğan ◽  
Süleyman Taşpınar
Keyword(s):  
Spatial Dependence ◽  
Spatial Models ◽  
Test Statistics ◽  
Simple Test ◽  
Robust Test ◽  
Finite Samples ◽  
Robust Tests ◽  
Spatial Autoregressive ◽  
Spatial Autoregressive Models ◽  
Empirical Illustration

Abstract In this study, we propose simple test statistics for identifying the source of spatial dependence in spatial autoregressive models with endogenous weights matrices. Elements of the weights matrices are modelled in such a way that endogenity arises when the unobserved factors that affect elements of the weights matrices are correlated with the unobserved factors in the outcome equation. The proposed test statistics are robust to the presence of endogeneity in the weights and can be used to detect spatial dependence in the dependent variable and/or the disturbance terms. The robust test statistics are easy to calculate as computationally simple estimations are needed for their calculations. Our Monte Carlo results indicate that these tests have good size and power properties in finite samples. We also provide an empirical illustration to demonstrate the usefulness of the robust tests in identifying the source of spatial dependence.

scholarly journals ROBUST FORECAST COMPARISON

2016 ◽  
Vol 33 (6) ◽  
pp. 1306-1351 ◽  
Author(s):  
Sainan Jin ◽  
Valentina Corradi ◽  
Norman R. Swanson
Keyword(s):  
Loss Function ◽  
Forecast Accuracy ◽  
Forecast Errors ◽  
Local Power ◽  
Multiple Model ◽  
Test Statistics ◽  
Misspecified Models ◽  
Finite Samples ◽  
Out Of Sample ◽  
Forecast Comparison

Forecast accuracy is typically measured in terms of a given loss function. However, as a consequence of the use of misspecified models in multiple model comparisons, relative forecast rankings are loss function dependent. In order to address this issue, a novel criterion for forecast evaluation that utilizes the entire distribution of forecast errors is introduced. In particular, we introduce the concepts of general-loss (GL) forecast superiority and convex-loss (CL) forecast superiority; and we develop tests for GL (CL) superiority that are based on an out-of-sample generalization of the tests introduced by Linton, Maasoumi, and Whang (2005, Review of Economic Studies 72, 735–765). Our test statistics are characterized by nonstandard limiting distributions, under the null, necessitating the use of resampling procedures to obtain critical values. Additionally, the tests are consistent and have nontrivial local power, under a sequence of local alternatives. The above theory is developed for the stationary case, as well as for the case of heterogeneity that is induced by distributional change over time. Monte Carlo simulations suggest that the tests perform reasonably well in finite samples, and an application in which we examine exchange rate data indicates that our tests can help identify superior forecasting models, regardless of loss function.

Genetics of allozyme variation in Cuphea lanceolata Ait.

Genome ◽  
1989 ◽  
Vol 32 (1) ◽  
pp. 57-63 ◽  
Author(s):  
S. J. Knapp ◽  
L. A. Tagliani
Keyword(s):  
Goodness Of Fit ◽  
Capric Acid ◽  
Allozyme Variation ◽  
Test Statistics ◽  
Shikimate Dehydrogenase ◽  
Average Heterozygosity ◽  
Goodness Of Fit Test ◽  
Polymorphic Loci ◽  
Segregation Ratios ◽  
Cuphea Lanceolata

Genetic markers are needed for mating systems and breeding experiments in Cuphea lanceolata Ait.; however, none have been described in this species. Allozyme variation was analyzed among 14 F2 populations assayed for aconitase (ACO), diaphorase (DIA), esterase (EST), fluorescent esterase transaminase (FEST), glutamine oxaloacetate transaminase (GOT), menadione reductase (MNR), phosphoglucomutase (PGM), phosphoglucose isomerase (PGI), and shikimate dehydrogenase (SKDH) enzyme activity. At least 23 loci were resolved in these enzyme systems: 6 monomorphic loci, 5 poorly resolved loci, and 12 clearly resolved polymorphic loci. Observed segregation ratios were generally not significantly different (P > 0.05) from expected segregation ratios; however, segregation distortion was observed at Skdh-1 and Mnr-1 (Dia-1) in some F2 populations. Skdh-1 and Pgm-2 and Est-1, Est-2, Fest-1, and Mnr-1 comprise putative linkage groups. Allozyme variation was observed between and within accessions. The expected average heterozygosity was 16.3%. There were one to eight polymorphic loci among the F2 populations analyzed. There were an average of 2.05 alleles per locus. Several useful codominant markers were identified and a partial allozyme linkage map was constructed. Additional work is needed to revise and complete the map.Key words: Cuphea, isozymes, goodness of fit test statistics, lauric acid, capric acid.

Sign in / Sign up

Export Citation Format

Share Document