Estimating the order of sinusoidal models using the adaptively penalized likelihood approach: Large sample consistency properties
Recently, the paper [2] introduced a method for model order estimation based on penalizing adaptively the likelihood (PAL). In this paper, we use the PAL based order estimation method for a nonlinear sinusoidal model and study its asymptotic statistical properties. We prove that the estimator of the...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Surana, Khushboo [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2016 |
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| Schlagwörter: |
Bayesian information criterion (BIC) |
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| Umfang: |
8 |
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| Übergeordnetes Werk: |
Enthalten in: Influence of Sc3+ doping in B-site on electrochemical performance of Li4Ti5O12 anode materials for lithium-ion battery - Zhang, Yaoyao ELSEVIER, 2014transfer abstract, a European journal devoted to the methods and applications of signal processing, Amsterdam [u.a.] |
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| Übergeordnetes Werk: |
volume:128 ; year:2016 ; pages:204-211 ; extent:8 |
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| DOI / URN: |
10.1016/j.sigpro.2016.04.001 |
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ELV029516315 |
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| 520 | |a Recently, the paper [2] introduced a method for model order estimation based on penalizing adaptively the likelihood (PAL). In this paper, we use the PAL based order estimation method for a nonlinear sinusoidal model and study its asymptotic statistical properties. We prove that the estimator of the model order using the PAL rule is consistent. Simulation examples are presented to illustrate the performance of the PAL method for small sample sizes and to compare it with that of three information criterion-based methods. | ||
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Estimating the order of sinusoidal models using the adaptively penalized likelihood approach: Large sample consistency properties |
| abstract |
Recently, the paper [2] introduced a method for model order estimation based on penalizing adaptively the likelihood (PAL). In this paper, we use the PAL based order estimation method for a nonlinear sinusoidal model and study its asymptotic statistical properties. We prove that the estimator of the model order using the PAL rule is consistent. Simulation examples are presented to illustrate the performance of the PAL method for small sample sizes and to compare it with that of three information criterion-based methods. |
| abstractGer |
Recently, the paper [2] introduced a method for model order estimation based on penalizing adaptively the likelihood (PAL). In this paper, we use the PAL based order estimation method for a nonlinear sinusoidal model and study its asymptotic statistical properties. We prove that the estimator of the model order using the PAL rule is consistent. Simulation examples are presented to illustrate the performance of the PAL method for small sample sizes and to compare it with that of three information criterion-based methods. |
| abstract_unstemmed |
Recently, the paper [2] introduced a method for model order estimation based on penalizing adaptively the likelihood (PAL). In this paper, we use the PAL based order estimation method for a nonlinear sinusoidal model and study its asymptotic statistical properties. We prove that the estimator of the model order using the PAL rule is consistent. Simulation examples are presented to illustrate the performance of the PAL method for small sample sizes and to compare it with that of three information criterion-based methods. |
| collection_details |
GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_70 |
| title_short |
Estimating the order of sinusoidal models using the adaptively penalized likelihood approach: Large sample consistency properties |
| url |
https://doi.org/10.1016/j.sigpro.2016.04.001 |
| remote_bool |
true |
| author2 |
Mitra, Sharmishtha Mitra, Amit Stoica, Petre |
| author2Str |
Mitra, Sharmishtha Mitra, Amit Stoica, Petre |
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ELV017513162 |
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| hochschulschrift_bool |
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| author2_role |
oth oth oth |
| doi_str |
10.1016/j.sigpro.2016.04.001 |
| up_date |
2024-07-06T21:40:07.292Z |
| _version_ |
1803867397600313344 |
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