• 제목/요약/키워드: Maximum entropy distribution

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Bayesian and maximum likelihood estimation of entropy of the inverse Weibull distribution under generalized type I progressive hybrid censoring

  • Lee, Kyeongjun
    • Communications for Statistical Applications and Methods
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    • 제27권4호
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    • pp.469-486
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    • 2020
  • Entropy is an important term in statistical mechanics that was originally defined in the second law of thermodynamics. In this paper, we consider the maximum likelihood estimation (MLE), maximum product spacings estimation (MPSE) and Bayesian estimation of the entropy of an inverse Weibull distribution (InW) under a generalized type I progressive hybrid censoring scheme (GePH). The MLE and MPSE of the entropy cannot be obtained in closed form; therefore, we propose using the Newton-Raphson algorithm to solve it. Further, the Bayesian estimators for the entropy of InW based on squared error loss function (SqL), precautionary loss function (PrL), general entropy loss function (GeL) and linex loss function (LiL) are derived. In addition, we derive the Lindley's approximate method (LiA) of the Bayesian estimates. Monte Carlo simulations are conducted to compare the results among MLE, MPSE, and Bayesian estimators. A real data set based on the GePH is also analyzed for illustrative purposes.

노달방법의 중성자속 분포 재생 문제에의 최대 엔트로피 원리에 의한 새로운 접근 (A New Formulation of the Reconstruction Problem in Neutronics Nodal Methods Based on Maximum Entropy Principle)

  • Na, Won-Joon;Cho, Nam-Zin
    • Nuclear Engineering and Technology
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    • 제21권3호
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    • pp.193-204
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    • 1989
  • 본 논문에서는 정보 이론의 maximum entropy Principle을 이용하여 중성자속 분포를 재생하는 새로운 방법을 시도하였다. 어떤 대상에 대한 부분적인 정보가 있을 때, 이 정보의 한도 내에서 entropy를 최대화시키는 확률 분포는 가장 객관적인 것이 된다. Nodal method계산결과인 평균 중성자속과 current의 값을 prior information으로 삼고, 핵 연료 집합체의 경계에서의 중성자속 분포를 확률의 형태로 변환해서 확률로써 다룬다. Prior information의 한도 내에서 entropy를 최대화시키는 경계에서의 확률 분포를 구하면 핵연료 집합체의 경계에서의 중성자속 분포가 구해지는데, 이것을 경계조건으로 heterogeneous assembly calculation을 행하여 세부적인 중성자속 분포를 구한다. 이 새로운 방법을 몇 개의 benchmark problem assembly에 응용해 본 결과, 노심의 안쪽 부분에서는 이 방법이 form function method에 의한 것과 비슷한 정확도를 보였고 바깥 부분에서는 다소 큰 오차를 보였다. 본 논문에서는 surface-averaged neutron current를 prior in-formation에 포함시키지 못했는데, 이것을 포함시키면 결과가 훨씬 개선 될 것으로 보인다.

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Discriminant Analysis of Binary Data by Using the Maximum Entropy Distribution

  • Lee, Jung Jin;Hwang, Joon
    • Communications for Statistical Applications and Methods
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    • 제10권3호
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    • pp.909-917
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    • 2003
  • Although many classification models have been used to classify binary data, none of the classification models dominates all varying circumstances depending on the number of variables and the size of data(Asparoukhov and Krzanowski (2001)). This paper proposes a classification model which uses information on marginal distributions of sub-variables and its maximum entropy distribution. Classification experiments by using simulation are discussed.

Minimum Variance Unbiased Estimation for the Maximum Entropy of the Transformed Inverse Gaussian Random Variable by Y=X-1/2

  • Choi, Byung-Jin
    • Communications for Statistical Applications and Methods
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    • 제13권3호
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    • pp.657-667
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    • 2006
  • The concept of entropy, introduced in communication theory by Shannon (1948) as a measure of uncertainty, is of prime interest in information-theoretic statistics. This paper considers the minimum variance unbiased estimation for the maximum entropy of the transformed inverse Gaussian random variable by $Y=X^{-1/2}$. The properties of the derived UMVU estimator is investigated.

A COMPARATIVE EVALUATION OF THE ESTIMATORS OF THE 2-PARAMETER GENERALIZED PARETO DISTRIBUTION

  • Singh, V.P.;Ahmad, M.;Sherif, M.M.
    • Water Engineering Research
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    • 제4권3호
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    • pp.155-173
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    • 2003
  • Parameters and quantiles of the 2-parameter generalized Pareto distribution were estimated using the methods of regular moments, modified moments, probability weighted moments, linear moments, maximum likelihood, and entropy for Monte Carlo-generated samples. The performance of these seven estimators was statistically compared, with the objective of identifying the most robust estimator. It was found that in general the methods of probability-weighted moments and L-moments performed better than the methods of maximum likelihood estimation, moments and entropy, especially for smaller values of the coefficient of variation and probability of exceedance.

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Generalized half-logistic Poisson distributions

  • Muhammad, Mustapha
    • Communications for Statistical Applications and Methods
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    • 제24권4호
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    • pp.353-365
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    • 2017
  • In this article, we proposed a new three-parameter distribution called generalized half-logistic Poisson distribution with a failure rate function that can be increasing, decreasing or upside-down bathtub-shaped depending on its parameters. The new model extends the half-logistic Poisson distribution and has exponentiated half-logistic as its limiting distribution. A comprehensive mathematical and statistical treatment of the new distribution is provided. We provide an explicit expression for the $r^{th}$ moment, moment generating function, Shannon entropy and $R{\acute{e}}nyi$ entropy. The model parameter estimation was conducted via a maximum likelihood method; in addition, the existence and uniqueness of maximum likelihood estimations are analyzed under potential conditions. Finally, an application of the new distribution to a real dataset shows the flexibility and potentiality of the proposed distribution.

Discriminant Analysis of Binary Data with Multinomial Distribution by Using the Iterative Cross Entropy Minimization Estimation

  • Lee Jung Jin
    • Communications for Statistical Applications and Methods
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    • 제12권1호
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    • pp.125-137
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    • 2005
  • Many discriminant analysis models for binary data have been used in real applications, but none of the classification models dominates in all varying circumstances(Asparoukhov & Krzanowski(2001)). Lee and Hwang (2003) proposed a new classification model by using multinomial distribution with the maximum entropy estimation method. The model showed some promising results in case of small number of variables, but its performance was not satisfactory for large number of variables. This paper explores to use the iterative cross entropy minimization estimation method in replace of the maximum entropy estimation. Simulation experiments show that this method can compete with other well known existing classification models.

Estimation of entropy of the inverse weibull distribution under generalized progressive hybrid censored data

  • Lee, Kyeongjun
    • Journal of the Korean Data and Information Science Society
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    • 제28권3호
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    • pp.659-668
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    • 2017
  • The inverse Weibull distribution (IWD) can be readily applied to a wide range of situations including applications in medicines, reliability and ecology. It is generally known that the lifetimes of test items may not be recorded exactly. In this paper, therefore, we consider the maximum likelihood estimation (MLE) and Bayes estimation of the entropy of a IWD under generalized progressive hybrid censoring (GPHC) scheme. It is observed that the MLE of the entropy cannot be obtained in closed form, so we have to solve two non-linear equations simultaneously. Further, the Bayes estimators for the entropy of IWD based on squared error loss function (SELF), precautionary loss function (PLF), and linex loss function (LLF) are derived. Since the Bayes estimators cannot be obtained in closed form, we derive the Bayes estimates by revoking the Tierney and Kadane approximate method. We carried out Monte Carlo simulations to compare the classical and Bayes estimators. In addition, two real data sets based on GPHC scheme have been also analysed for illustrative purposes.

Maximum entropy test for infinite order autoregressive models

  • Lee, Sangyeol;Lee, Jiyeon;Noh, Jungsik
    • Journal of the Korean Data and Information Science Society
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    • 제24권3호
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    • pp.637-642
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    • 2013
  • In this paper, we consider the maximum entropy test in in nite order autoregressiv models. Its asymptotic distribution is derived under the null hypothesis. A bootstrap version of the test is discussed and its performance is evaluated through Monte Carlo simulations.

Modeling the Spatial Distribution of Black-Necked Cranes in Ladakh Using Maximum Entropy

  • Meenakshi Chauhan;Randeep Singh;Puneet Pandey
    • Proceedings of the National Institute of Ecology of the Republic of Korea
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    • 제4권2호
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    • pp.79-85
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    • 2023
  • The Tibetan Plateau is home to the only alpine crane species, the black-necked crane (Grus nigricollis). Conservation efforts are severely hampered by a lack of knowledge on the spatial distribution and breeding habitats of this species. The ecological niche modeling framework used to predict the spatial distribution of this species, based on the maximum entropy and occurrence record data, allowed us to generate a species-specific spatial distribution map in Ladakh, Trans-Himalaya, India. The model was created by assimilating species occurrence data from 486 geographical sites with 24 topographic and bioclimatic variables. Fourteen variables helped forecast the distribution of black-necked cranes by 96.2%. The area under the curve score for the model training data was high (0.98), indicating the accuracy and predictive performance of the model. Of the total study area, the areas with high and moderate habitat suitability for black-necked cranes were anticipated to be 8,156 km2 and 6,759 km2, respectively. The area with high habitat suitability within the protected areas was 5,335 km2. The spatial distribution predicted using our model showed that the majority of speculated conservation areas bordered the existing protected areas of the Changthang Wildlife Sanctuary. Hence, we believe, that by increasing the current study area, we can account for these gaps in conservation areas, more effectively.