Study of Change-Point Detection and Applications Based on Several Statistical Methods
Αποθηκεύτηκε σε:
| Εκδόθηκε σε: | Symmetry vol. 17, no. 2 (2025), p. 302 |
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| Κύριος συγγραφέας: | |
| Άλλοι συγγραφείς: | , , |
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MDPI AG
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| Διαθέσιμο Online: | Citation/Abstract Full Text + Graphics Full Text - PDF |
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| 022 | |a 2073-8994 | ||
| 024 | 7 | |a 10.3390/sym17020302 |2 doi | |
| 035 | |a 3171256753 | ||
| 045 | 2 | |b d20250101 |b d20251231 | |
| 084 | |a 231635 |2 nlm | ||
| 100 | 1 | |a Tian, Fenglin |u School of Mathematics, Harbin Institute of Technology, Harbin 150001, China; <email>tfl_hit@126.com</email> (F.T.); | |
| 245 | 1 | |a Study of Change-Point Detection and Applications Based on Several Statistical Methods | |
| 260 | |b MDPI AG |c 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a In the current global context of economic integration, unexpected events have an important influence in the financial field. In 2020, the “COVID-19” outbreak triggered financial turmoil throughout the whole country and even in the global market. In the wake of this era, how to sum up past developments and predict future development through change-point detection is particularly important. In this paper, four methods for detecting change-points are presented: the likelihood ratio method, least squares method, CUSUM method, and local comparison method. Considering that Bernstein polynomials have worked well in density function approximation, the multi-dimensional Bernstein polynomials are presented. The study applies multiple change-point detection methods to determine the most suitable degree of freedom <inline-formula>mj</inline-formula> for multi-dimensional Bernstein models, after which various rewriting expressions can be obtained. Next, “COVID-19” data and money supply data are used for change-point detection with good results. Then, we focus on conducting change-point testing on the S&P 500 index and SSE 50 index, indicating strong symmetry when major crisis events occur. All analyses indicate that change-point detection plays an important role in identifying major crisis events and financial shocks. | |
| 651 | 4 | |a United States--US | |
| 653 | |a Random variables | ||
| 653 | |a Hypotheses | ||
| 653 | |a Foreign exchange markets | ||
| 653 | |a Normal distribution | ||
| 653 | |a Maximum likelihood method | ||
| 653 | |a Polynomials | ||
| 653 | |a Least squares method | ||
| 653 | |a Statistical methods | ||
| 653 | |a Approximation | ||
| 653 | |a Earthquakes | ||
| 653 | |a Multidimensional methods | ||
| 653 | |a Time series | ||
| 653 | |a Density functions | ||
| 653 | |a Test methods | ||
| 653 | |a Economics | ||
| 653 | |a Statistical analysis | ||
| 653 | |a Likelihood ratio | ||
| 700 | 1 | |a Yue Qi |u Student Affairs Department, ShanghaiTech University, Shanghai 201210, China | |
| 700 | 1 | |a Wang, Yong |u School of Mathematics, Harbin Institute of Technology, Harbin 150001, China; <email>tfl_hit@126.com</email> (F.T.); | |
| 700 | 1 | |a Tian, Boping |u School of Mathematics, Harbin Institute of Technology, Harbin 150001, China; <email>tfl_hit@126.com</email> (F.T.); | |
| 773 | 0 | |t Symmetry |g vol. 17, no. 2 (2025), p. 302 | |
| 786 | 0 | |d ProQuest |t Engineering Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3171256753/abstract/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text + Graphics |u https://www.proquest.com/docview/3171256753/fulltextwithgraphics/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3171256753/fulltextPDF/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |