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Estimating parameters in multichannel fundamental frequency with harmonics model 用谐波模型估计多通道基频参数

IF 1.9 4区数学

Statistics Pub Date : 2023-09-03 DOI: 10.1080/02331888.2023.2253992

Swagata Nandi, D. Kundu

{"title":"Estimating parameters in multichannel fundamental frequency with harmonics model","authors":"Swagata Nandi, D. Kundu","doi":"10.1080/02331888.2023.2253992","DOIUrl":"https://doi.org/10.1080/02331888.2023.2253992","url":null,"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.","PeriodicalId":54358,"journal":{"name":"Statistics","volume":"14 1","pages":"1142 - 1164"},"PeriodicalIF":1.9,"publicationDate":"2023-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80029696","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Impact assessment of correlated measurement errors using logarithmic-type estimators 使用对数型估计器对相关测量误差进行影响评估

4区数学

Statistics Pub Date : 2023-09-03 DOI: 10.1080/02331888.2023.2260915

Shashi Bhushan, Anoop Kumar, Shivam Shukla

引用次数: 1

Penalized wavelet nonparametric univariate logistic regression for irregular spaced data 不规则间距数据的惩罚小波非参数单变量逻辑回归

IF 1.9 4区数学

Statistics Pub Date : 2023-08-29 DOI: 10.1080/02331888.2023.2248679

U. Amato, A. Antoniadis, I. De Feis, I. Gijbels

引用次数: 0

On optimal joint prediction of order statistics 序统计量的最优联合预测

IF 1.9 4区数学

Statistics Pub Date : 2023-08-27 DOI: 10.1080/02331888.2023.2249572

N. Balakrishnan, R. Mukerjee

引用次数: 0

Parameter estimation in optional semimartingale regression models 可选半鞅回归模型参数估计

IF 1.9 4区数学

Statistics Pub Date : 2023-08-01 DOI: 10.1080/02331888.2023.2242549

A. Melnikov, A. Pak

引用次数: 0

Corrigendum: a note on Liu-type shrinkage estimations in linear models (Statistics 56, 396–420) 更正:关于线性模型中刘氏型收缩估计的注释(统计学56,396-420)

IF 1.9 4区数学

Statistics Pub Date : 2023-07-31 DOI: 10.1080/02331888.2023.2234061

S. Nkurunziza

引用次数: 0

Characterization-based approach for construction of goodness-of-fit test for Lévy distribution 基于特征的lsamvy分布拟合优度检验构造方法

IF 1.9 4区数学

Statistics Pub Date : 2023-07-24 DOI: 10.1080/02331888.2023.2238236

Žikica Lukić, B. Milošević

引用次数: 0

Optimal subsampling algorithms for composite quantile regression in massive data 海量数据中复合分位数回归的最优次抽样算法

IF 1.9 4区数学

Statistics Pub Date : 2023-07-04 DOI: 10.1080/02331888.2023.2239507

Jun Jin, Shuangzhe Liu, Tiefeng Ma

{"title":"Optimal subsampling algorithms for composite quantile regression in massive data","authors":"Jun Jin, Shuangzhe Liu, Tiefeng Ma","doi":"10.1080/02331888.2023.2239507","DOIUrl":"https://doi.org/10.1080/02331888.2023.2239507","url":null,"abstract":"Massive datasets have gained increasing prominence across various fields, but their analysis is often impeded by computational limitations. In response, Wang and Ma (Optimal subsampling for quantile regression in big data. Biometrika. 2021;108:99–112) have proposed an optimal subsampling method for quantile regression in massive datasets. Composite quantile regression, as a robust and efficient alternative to ordinary least squares regression and quantile regression in linear models, presents further complexities due to its distinct loss function. This paper extends the optimal subsampling method to accommodate composite quantile regression problems. We begin by deriving two new optimal subsampling probabilities for composite quantile regression, considering both the L- and A-optimality criteria Subsequently, we develop an adaptive two-step method based on these probabilities. The resulting estimators exhibit desirable asymptotic properties. In addition, to estimate the variance-covariance matrix without explicitly estimating the densities of the responses, we propose a combining subsamples method. Numerical studies on simulated and real data are conducted to assess and showcase the practical performance of our proposed methods.","PeriodicalId":54358,"journal":{"name":"Statistics","volume":"29 1","pages":"811 - 843"},"PeriodicalIF":1.9,"publicationDate":"2023-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79520910","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Dimension reduction techniques for conditional expectiles 条件期望项的降维技术

IF 1.9 4区数学

Statistics Pub Date : 2023-07-04 DOI: 10.1080/02331888.2023.2236745

Eliana Christou

{"title":"Dimension reduction techniques for conditional expectiles","authors":"Eliana Christou","doi":"10.1080/02331888.2023.2236745","DOIUrl":"https://doi.org/10.1080/02331888.2023.2236745","url":null,"abstract":"Marginalizing the importance of characterizing tail events can lead to catastrophic repercussions. Look no further than examples from meteorology and climatology (polar reversals, natural disasters), economics (2008 subprime mortgage crisis), or even medical-diagnostics (low/high risk patients in survival analysis). Investigating these events can become even more challenging when working with high-dimensional data, making it necessary to use dimension reduction techniques. Although research has recently turned to dimension reduction techniques that use conditional quantiles, there is a surprisingly limited amount of research dedicated to the underexplored research area of expectile regression (ER). Therefore, we present the first comprehensive work about dimension reduction techniques for conditional expectiles. Specifically, we introduce the central expectile subspace, i.e., the space that spans the fewest linear combinations of the predictors that contain all the information about the response that is available from the conditional expectile. We then introduce a nonlinear extension of the proposed methodology that extracts nonlinear features. The performance of the algorithms are demonstrated through extensive simulation examples and a real data application. The results suggest that ER is an effective tool for describing tail events and is a competitive alternative to quantile regression.","PeriodicalId":54358,"journal":{"name":"Statistics","volume":"28 1","pages":"960 - 985"},"PeriodicalIF":1.9,"publicationDate":"2023-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81780238","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Fiducialize statistical significance: transforming p-values into conservative posterior probabilities and Bayes factors fiducalize统计显著性:将p值转换为保守后验概率和贝叶斯因子

IF 1.9 4区数学

Statistics Pub Date : 2023-07-04 DOI: 10.1080/02331888.2023.2232912

D. Bickel

{"title":"Fiducialize statistical significance: transforming p-values into conservative posterior probabilities and Bayes factors","authors":"D. Bickel","doi":"10.1080/02331888.2023.2232912","DOIUrl":"https://doi.org/10.1080/02331888.2023.2232912","url":null,"abstract":"One remedy to the misuse of p-values transforms them to bounds on Bayes factors. With a prior probability of the null hypothesis, such a bound gives a lower bound on the posterior probability. Unfortunately, knowing a posterior probability is above some number cannot ensure that the null hypothesis is improbable enough to warrant its rejection. For example, if the lower bound is 0.0001, that implies that the posterior probability is at least 0.0001 but does not imply it is lower than 0.05 or even 0.9. A fiducial argument suggests an alternative estimate of the posterior probability that the null hypothesis is true. In the case that the prior probability of the null hypothesis is 50%, the estimated posterior probability is about for low p. In other cases, each occurrence of in the formula is the p-value calibrated by multiplying it by the prior odds of the null hypothesis. In the absence of a prior, also serves as an asymptotic Bayes factor. Since the fiducial estimate of the posterior probability is greater than the lower bounds, its use in place of a bound leads to more stringent hypothesis testing. Making that replacement in a rationale for 0.005 as the significance level reduces the level to 0.001.","PeriodicalId":54358,"journal":{"name":"Statistics","volume":"114 1","pages":"941 - 959"},"PeriodicalIF":1.9,"publicationDate":"2023-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79420770","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1