scholarly journals R2ucare: An R package to perform goodness-of-fit tests for capture-recapture models

2017 ◽  
Author(s):  
Olivier Gimenez ◽  
Jean-Dominique Lebreton ◽  
Remi Choquet ◽  
Roger Pradel
Keyword(s):  
Statistical Model ◽  
Goodness Of Fit ◽  
Real Data ◽  
R Package ◽  
Goodness Of Fit Tests ◽  
State Capture ◽  
Capture Recapture ◽  
Quality Of Fit

Assessing the quality of fit of a statistical model to data is a necessary step for conducting safe inference. We introduce R2ucare, an R package to perform goodness-of-fit tests for open single- and multi-state capture-recapture models. R2ucare also has various functions to manipulate capture-recapture data. We remind the basics and provide guidelines to navigate towards testing the fit of capture-recapture models. We demonstrate the functionality of R2ucare through its application to real data.

scholarly journals R2ucare: An r package to perform goodness‐of‐fit tests for capture–recapture models

2018 ◽  
Vol 9 (7) ◽  
pp. 1749-1754 ◽  
Author(s):  
Olivier Gimenez ◽  
Jean‐Dominique Lebreton ◽  
Rémi Choquet ◽  
Roger Pradel
Keyword(s):  
Goodness Of Fit ◽  
R Package ◽  
Goodness Of Fit Tests ◽  
Capture Recapture

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 New Shelf-Life Failure Model

1981 ◽  
Vol 44 (8) ◽  
pp. 580-580
Author(s):  
G. J. NEWELL
Keyword(s):  
Statistical Model ◽  
Shelf Life ◽  
Goodness Of Fit ◽  
Ad Hoc ◽  
Failure Process ◽  
Physical Characteristics ◽  
Failure Model ◽  
Goodness Of Fit Tests

A new statistical model for shelf-life failure is proposed. This model is based on consideration of the basic physical characteristics of the shelf-life failure process rather than ad hoc reasons such as goodness-of-fit tests.

Stochastic model of contractions at a point in the duodenum

1975 ◽  
Vol 229 (3) ◽  
pp. 613-617 ◽  
Author(s):  
RB Singerman ◽  
EO Macagno ◽  
Glover ◽  
J Christensen
Keyword(s):  
Slow Wave ◽  
Goodness Of Fit ◽  
Human Subjects ◽  
Real Data ◽  
Type Model ◽  
Chi Square ◽  
Frequency Distributions ◽  
The Real ◽  
Goodness Of Fit Tests ◽  
Wave Cycle

Contractions at one point in the human duodenum were studied as a time series. Manometric records were made over long time periods from the duodenum in fed human subjects. A 5-s grid was superimposed on the time axis of the records. Each 5-s interval was treated as a slow-wave cycle within which either a contraction or a no-contraction could occur. The resulting series of alternating runs of contractions and no-contractions was tested for the existence of trends. Trends were found indicating possible temporal dependence. A Markov-type model was used to try to generate data similar to the real data. Success was achieved by a model that assumed a probability of contraction dependent on the three previous slow-wave cycles. The frequency distributions obtained from the real and generated data were compared using Chi-square goodness-of-fit tests and found to be statistically similar. The correlations in time found for the contractions might be due to a time dependency in the controls for contraction over four successive slow-wave periods, 20 s in humans.

ESTIMATION OF EXTREME QUANTILES: EMPIRICAL TOOLS FOR METHODS ASSESSMENT AND COMPARISON

2000 ◽  
Vol 07 (01) ◽  
pp. 75-94 ◽  
Author(s):  
J. DIEBOLT ◽  
M.-A. EL-AROUI ◽  
V. DURBEC ◽  
B. VILLAIN
Keyword(s):  
Goodness Of Fit ◽  
Simulated Data ◽  
Real Data ◽  
Data Sets ◽  
Data Set ◽  
Extreme Quantiles ◽  
Maintenance Policies ◽  
Simulated Data Sets ◽  
Industrial Context

When extreme quantiles have to be estimated from a given data set, the classical parametric approach can lead to very poor estimations. This has led to the introduction of specific methods for estimating extreme quantiles (MEEQ's) in a nonparametric spirit, e.g., Pickands excess method, methods based on Hill's estimate of the Pareto index, exponential tail (ET) and quadratic tail (QT) methods. However, no practical technique for assessing and comparing these MEEQ's when they are to be used on a given data set is available. This paper is a first attempt to provide such techniques. We first compare the estimations given by the main MEEQ's on several simulated data sets. Then we suggest goodness-of-fit (Gof) tests to assess the MEEQ's by measuring the quality of their underlying approximations. It is shown that Gof techniques bring very relevant tools to assess and compare ET and excess methods. Other empirical criterions for comparing MEEQ's are also proposed and studied through Monte-Carlo analyses. Finally, these assessment and comparison techniques are experimented on real-data sets issued from an industrial context where extreme quantiles are needed to define maintenance policies.

scholarly journals Goodness-of-fit tests for the beta Gompertz distribution

2020 ◽  
Vol 24 (Suppl. 1) ◽  
pp. 69-81
Author(s):  
Hanaa Abu-Zinadah ◽  
Asmaa Binkhamis
Keyword(s):  
Goodness Of Fit ◽  
Real Data ◽  
Likelihood Method ◽  
Test Statistics ◽  
Gompertz Distribution ◽  
Goodness Of Fit Tests ◽  
Lilliefors Test ◽  
Kolmogorov Smirnov ◽  
Anderson Darling ◽  
Von Mises

This article studied the goodness-of-fit tests for the beta Gompertz distribution with four parameters based on a complete sample. The parameters were estimated by the maximum likelihood method. Critical values were found by Monte Carlo simulation for the modified Kolmogorov-Smirnov, Anderson-Darling, Cramer-von Mises, and Lilliefors test statistics. The power of these test statistics founded the optimal alternative distribution. Real data applications were used as examples for the goodness of fit tests.

scholarly journals Nonparametric independence testing via mutual information

Biometrika ◽  
2019 ◽  
Vol 106 (3) ◽  
pp. 547-566 ◽  
Author(s):  
T B Berrett ◽  
R J Samworth
Keyword(s):  
Mutual Information ◽  
Goodness Of Fit ◽  
Linear Models ◽  
Real Data ◽  
Nearest Neighbour ◽  
Local Power ◽  
Goodness Of Fit Tests ◽  
Numerical Studies ◽  
Independence Testing ◽  
Power Analyses

Summary We propose a test of independence of two multivariate random vectors, given a sample from the underlying population. Our approach is based on the estimation of mutual information, whose decomposition into joint and marginal entropies facilitates the use of recently developed efficient entropy estimators derived from nearest neighbour distances. The proposed critical values may be obtained by simulation in the case where an approximation to one marginal is available or by permuting the data otherwise. This facilitates size guarantees, and we provide local power analyses, uniformly over classes of densities whose mutual information satisfies a lower bound. Our ideas may be extended to provide new goodness-of-fit tests for normal linear models based on assessing the independence of our vector of covariates and an appropriately defined notion of an error vector. The theory is supported by numerical studies on both simulated and real data.

scholarly journals A Density-Based Empirical Likelihood Ratio Approach for Goodness-of-fit Tests in Decreasing Densities

2020 ◽  
Vol 8 (1) ◽  
pp. 66-79
Author(s):  
Vahid Fakoor ◽  
Masoud Ajami ◽  
Seyed Mahdi Amir Jahanshahi ◽  
Ali Shariati
Keyword(s):  
Empirical Likelihood ◽  
Null Hypothesis ◽  
Goodness Of Fit ◽  
Real Data ◽  
Distribution Functions ◽  
Test Statistic ◽  
Goodness Of Fit Tests ◽  
Alternative Hypotheses ◽  
Ratio Approach ◽  
Consistency Of The Test

In this paper, we propose a test for the null hypothesis that a decreasing density function belongs to a givenparametric family of distribution functions against the non-parametric alternative. This method, which is based on an empirical likelihood (EL) ratio statistic, is similar to the test introduced by Vexler and Gurevich [23]. The consistency of the test statistic proposed is derived under the null and alternative hypotheses. A simulation study is conducted to inspect the power of the proposed test under various decreasing alternatives. In each scenario, the critical region of the test is obtained using a Monte Carlo technique. The applicability of the proposed test in practice is demonstrated through a few real data examples.  

scholarly journals A simplex dispersion model for improving precision in the odds ratio confidence interval in mixture experiments

2020 ◽  
Vol 42 ◽  
pp. e44068
Author(s):  
Gilberto Rodrigues Liska ◽  
Marcelo Ângelo Citillo ◽  
Fortunato Silva de Menezes ◽  
Júlio Silvio de Sousa Bueno Filho
Keyword(s):  
Confidence Intervals ◽  
Odds Ratio ◽  
Goodness Of Fit ◽  
Dispersion Model ◽  
Mammary Glands ◽  
Dispersion Models ◽  
Mixture Experiments ◽  
New Approach ◽  
Quality Of Fit

A new approach to data analysis in mixture experiments is proposed using the simplex regression, that is in the class of dispersion models family. The advantages of this approach are illustrated in an experiment studying the mixture effect of fat, carbohydrate, and fiber on tumors’ proportion in mammary glands of rats. Model was evaluated by goodness of fit criteria, simulated envelope charts for residuals of adjusted models, odds ratios graphics and their respective confidence intervals. The simplex regression model showed better quality of fit and smaller odds ratio confidence intervals.

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