Estimating parameters in multichannel fundamental frequency with harmonics model

IF 1.2 4区 数学 Q2 STATISTICS & PROBABILITY Statistics Pub Date : 2023-09-03 DOI:10.1080/02331888.2023.2253992
Swagata Nandi, D. Kundu
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Abstract

In this paper, we introduce a special multichannel model in the class of multichannel sinusoidal model. In multichannel sinusoidal model, the inherent frequencies from distinct channels are the same with different amplitudes. The underlying assumption here is that there is a fundamental frequency that is the same in each channel and the effective frequencies are harmonics of this fundamental frequency. We name this model as multichannel fundamental frequency with harmonics model. It is assumed that the errors in individual channel are independently and identically distributed whereas the signal from different channels are correlated. We propose generalized least squares estimators which become the maximum likelihood estimators also, when the error distribution of the different channels follows a multivariate Gaussian distribution. The proposed estimators are strongly consistent and asymptotically normally distributed. We have provided the implementation of the generalized least squares estimators in practice. Special attention has been taken when the number of channels is two and both have equal number of components. Simulation experiments have been carried out to observe the performances of the proposed estimators. Real data sets have been analysed using a two-channel fundamental frequency model.
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用谐波模型估计多通道基频参数
本文在多通道正弦模型中引入了一种特殊的多通道模型。在多通道正弦模型中,不同通道的固有频率相同,但振幅不同。这里的基本假设是每个通道都有一个相同的基频,有效频率是这个基频的谐波。我们将此模型称为多通道基频带谐波模型。假设各个信道的误差是独立的、同分布的,而不同信道的信号是相关的。当不同信道的误差分布服从多元高斯分布时,我们提出广义最小二乘估计,它也成为极大似然估计。所提出的估计量是强相合且渐近正态分布的。在实践中给出了广义最小二乘估计量的实现。特别注意的是,当通道数量为两个,并且两个通道都具有相同数量的组件时。通过仿真实验观察了所提估计器的性能。使用双通道基频模型对实际数据集进行了分析。
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来源期刊
Statistics
Statistics 数学-统计学与概率论
CiteScore
1.00
自引率
0.00%
发文量
59
审稿时长
12 months
期刊介绍: Statistics publishes papers developing and analysing new methods for any active field of statistics, motivated by real-life problems. Papers submitted for consideration should provide interesting and novel contributions to statistical theory and its applications with rigorous mathematical results and proofs. Moreover, numerical simulations and application to real data sets can improve the quality of papers, and should be included where appropriate. Statistics does not publish papers which represent mere application of existing procedures to case studies, and papers are required to contain methodological or theoretical innovation. Topics of interest include, for example, nonparametric statistics, time series, analysis of topological or functional data. Furthermore the journal also welcomes submissions in the field of theoretical econometrics and its links to mathematical statistics.
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