Parameter Estimation of Change Point Models based on the Discrete and Continuous Sampler

Xu, Xiaopeng; Liu, Chuancai; Zhang, Xiaochun

doi:10.1109/qrs-c55045.2021.00039

Cited by 1 publication

(1 citation statement)

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“…In these studies, the methodologies have been handled to create a new method to improve parameter estimation qualities in each parametric model in each research field in each situation under some assumptions. Some of these studies are; improving Bayes estimation via sparse sum of squares relaxations [43], improving maximum likelihood estimation via sparse sum of squares relaxations [44], improving parameter estimation of change point models via using Poisson distribution as discrete in the exponential changes as continuous sampler [45] and improving parameter estimation quality via an Experimental Design methodology [46].…”

Section: Literature Reviewmentioning

confidence: 99%

A Method to Improve Parameter Estimation Success

Unozkan

2023

IEEE Access

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This paper introduces a method for improving parameter estimation in statistical models. Parameter estimation is a popular area of study in statistics, and recent years have seen the introduction of new distributions with more parameters to enhance modelling success. While finding a suitable model for a dataset is crucial, accurately estimating parameters is equally important. In some cases, classical parameter estimation methods fail to provide a closed form of estimation for parameters. As a result, researchers commonly resort to numerical methods and software programs for parameter estimation in models. The success rates of models have gained significance with the rising popularity of novel techniques like machine learning algorithms and artificial neural networks. Robust and reliable models are built on the strong theoretical foundations of statistical distributions. Specific distributions are used in various research fields to model datasets, and the assumptions associated with these distributions provide valuable insights into observations. Additionally, parameter estimation results sometimes lead researchers to direct conclusions. This paper presents an improvement method that relies on the estimation of parameters from other statistical distributions. This novel approach aims to make parameter estimation easier and more successful in certain situations. In the applications in this paper, the proposed methodology improves the success rate by up to 10% which provides an additional 6% success in the models.

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Section: Literature Reviewmentioning

confidence: 99%

A Method to Improve Parameter Estimation Success

Unozkan

2023

IEEE Access

View full text Add to dashboard Cite

show abstract

Parameter Estimation of Change Point Models based on the Discrete and Continuous Sampler

Cited by 1 publication

References 14 publications

A Method to Improve Parameter Estimation Success

A Method to Improve Parameter Estimation Success

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