Correcting moments for goodness of fit tests based on two entropy estimates

Park, Sangun; Park, Dongryeon

doi:10.1080/00949650306245

Cited by 6 publications

(7 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…is the underlying density estimator of the sample entropy (Park and Park, 2003). We can instantly see the defectiveness in both tails of f mn (x) for a finite sample size; The (i/n) th quantile is overestimated for i < m + 1 and underestimated for i > n − m since x i:n 's are replaced with x 1:n and x n:n for i < 1 and i > n, respectively.…”

Section: Tail Behaviors Of Sample Entropymentioning

confidence: 99%

“…There have also been some approaches based on the direct estimation of (1.1). Vasicek (1976) introduced the sample entropy based on the spacings of order statistics, whose underlying nonparametric density estimator was later discovered by Park and Park (2003). Ebrahimi et al (1994) modified the sample entropy, and an approach based on linear regression was also provided by Correa (1995).…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Improving Sample Entropy Based on Nonparametric Quantile Estimation

Park¹,

Park²

2011

Communications for Statistical Applications and Methods

View full text Add to dashboard Cite

Sample entropy (Vasicek, 1976) has poor performance, and several nonparametric entropy estimators have been proposed as alternatives. In this paper, we consider a piecewise uniform density function based on quantiles, which enables us to evaluate entropy in each interval, and study the poor performance of the sample entropy in terms of the poor estimation of lower and upper quantiles. Then we propose some improved entropy estimators by simply modifying the quantile estimators, and compare their performances with some existing estimators.

show abstract

Section: Tail Behaviors Of Sample Entropymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Improving Sample Entropy Based on Nonparametric Quantile Estimation

Park¹,

Park²

2011

Communications for Statistical Applications and Methods

View full text Add to dashboard Cite

show abstract

“…Ebrahimi et al (1992) introduced a test procedure which exploit the Kullback-Leibler information and estimated the test statistic by entropy estimator of Vasicek (1976). Following their method, more test statistics were proposed by Grzegorzewski and Wieczorkowski (1999), Park and Park (2003), Choi et al (2004), Yousefzadeh and Arghami (2008), Gurevich and Davidson (2008), Alizadeh Noughabi and Arghami (2011b) and Zamanzade and Arghami (2011) based on different entropy estimators including estimators of Vasicek (1976), Van-Es (1992), Ebrahimi et al (1994), Correa (1995) and Alizadeh Noughabi (2010). Vexler and Gurevich (2010) and Gurevich and Vexler (2011) developed empirical likelihood ratio tests for goodness of fit and demonstrated that the well-known goodness of fit tests based on sample entropy and KullbackLeibler information are a product of the proposed empirical likelihood methodology.…”

Section: Introductionmentioning

confidence: 99%

Testing Exponentiality Based on Renyi Entropy of Transformed Data

Abbasnejad¹

2012

JSRI

View full text Add to dashboard Cite

Abstract. In this paper, we introduce new tests for exponentiality based on estimators of Renyi entropy of a continuous random variable. We first consider two transformations of the observations which turn the test of exponentiality into one of uniformity and use a corresponding test based on Renyi entropy. Critical values of the test statistics are computed by Monte Carlo simulations. Then, we compare powers of the tests for various alternatives and sample sizes with exponentiality tests based on Kullback-Leibler information proposed by Ebrahimi et al. (1992) and Choi et al. (2004). Our simulation results show that the proposed tests have higher powers than the competitor tests.

show abstract

“…Hence, several nonparametric density function estimators (Theil, 1980;Dudewicz and van der Meulen, 1981;Bowman, 1992;Park and Park, 2003) have been considered in estimating KL information, but we have to determine the bandwidth or the gap of order statistics.…”

Section: Introductionmentioning

confidence: 99%

Kullback-Leibler Information of the Equilibrium Distribution Function and its Application to Goodness of Fit Test

Park

Choi²,

Jung³

2014

Communications for Statistical Applications and Methods

Self Cite

View full text Add to dashboard Cite

Kullback-Leibler (KL) information is a measure of discrepancy between two probability density functions. However, several nonparametric density function estimators have been considered in estimating KL information because KL information is not well-defined on the empirical distribution function. In this paper, we consider the KL information of the equilibrium distribution function, which is well defined on the empirical distribution function (EDF), and propose an EDF-based goodness of fit test statistic. We evaluate the performance of the proposed test statistic for an exponential distribution with Monte Carlo simulation. We also extend the discussion to the censored case.

show abstract

Correcting moments for goodness of fit tests based on two entropy estimates

Cited by 6 publications

References 0 publications

Improving Sample Entropy Based on Nonparametric Quantile Estimation

Improving Sample Entropy Based on Nonparametric Quantile Estimation

Testing Exponentiality Based on Renyi Entropy of Transformed Data

Kullback-Leibler Information of the Equilibrium Distribution Function and its Application to Goodness of Fit Test

Contact Info

Product

Resources

About