Estimation of uncertainty distribution function by the principle of least squares

In order to estimate the unknown parameters in an uncertainty distribution function, this article uses the principle of least squares that minimizes the sum of the squared deviations between the uncertainty distribution and the empirical distribution of the observed data. After that, the principle o...

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Veröffentlicht in:Communications in statistics. Theory and methods Jg. 53; H. 21; S. 7624 - 7641
Hauptverfasser: Liu, Yang, Liu, Baoding
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Philadelphia Taylor & Francis 01.11.2024
Taylor & Francis Ltd
Schlagworte:
Crop yield
Differential equations
Distribution functions
Electricity pricing
Least squares
Parameter estimation
Parameter uncertainty
Regression models
uncertain differential equation
uncertain regression analysis
uncertain statistics
uncertain time series analysis
Uncertainty theory
ISSN:0361-0926, 1532-415X
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Zusammenfassung:In order to estimate the unknown parameters in an uncertainty distribution function, this article uses the principle of least squares that minimizes the sum of the squared deviations between the uncertainty distribution and the empirical distribution of the observed data. After that, the principle of least squares is applied to determining the uncertain disturbance term of uncertain regression model and uncertain time series model, and estimating the unknown parameters in uncertain differential equation. Finally, in order to illustrate the proposed method, some real-world examples are provided, including PetroChina stock price, electricity price, grain yield, China's population, and beef price.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:0361-0926
1532-415X
DOI:10.1080/03610926.2023.2269451