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Hybrid dynamic learning mechanism for multivariate time series segmentation Early View

  • Journal: Statistical Analysis and Data Mining: The ASA Data Science Journal
  • Authors: Ling Wang, Kang Li, Qian Ma, YanRong Lu
  • Published Date: Jan 25, 2020

Abstract To improve the efficiency of segmentation methods for multivariate time series, a hybrid dynamic learning mechanism for such series' segmentation is proposed. First, an incremental clustering...

A flexible procedure for mixture proportion estimation in positive‐unlabeled learning Early View

  • Journal: Statistical Analysis and Data Mining: The ASA Data Science Journal
  • Authors: Zhenfeng Lin, James P. Long
  • Published Date: Jan 11, 2020

Abstract Positive‐unlabeled (PU) learning considers two samples, a positive set P with observations from only one class and an unlabeled set U with observations from two classes. The goal is to...

Tree aggregation for random forest class probability estimation Early View

  • Journal: Statistical Analysis and Data Mining: The ASA Data Science Journal
  • Authors: Dan Nettleton, Ulrike Genschel, Andrew J. Sage
  • Published Date: Jan 02, 2020

Abstract In random forest methodology, an overall prediction or estimate is made by aggregating predictions made by individual decision trees. Popular implementations of random forests rely on...

Modeling bivariate long‐range dependence with general phase Early View

  • Journal: Journal of Time Series Analysis
  • Authors: Stefanos Kechagias, Vladas Pipiras
  • Published Date: Dec 15, 2019

Bivariate time series models are considered that are suitable for estimation, that have interpretable parameters and that can capture the general semi‐parametric formulation of bivariate long‐range...

A Portmanteau Test for Smooth Transition Autoregressive Models Early View

  • Journal: Journal of Time Series Analysis
  • Authors: Qiang Xia, Zhiqiang Zhang, Wai Keung Li
  • Published Date: Dec 02, 2019

This article investigates a portmanteau test statistic for checking model adequacy of smooth transition autoregressive (STAR) models. The asymptotic distribution of residual autocorrelations and the...

Location Multiplicative Error Models with Quasi Maximum Likelihood Estimation Early View

  • Journal: Journal of Time Series Analysis
  • Authors: Qian Li
  • Published Date: Dec 01, 2019

Motivated by improving the fitting of non‐negative financial time series, we extend the multiplicative error model and study the semi‐parametric estimation. We first introduce a location parameter and...

Consistency of the Hill Estimator for Time Series Observed with Measurement Errors Early View

  • Journal: Journal of Time Series Analysis
  • Authors: Mihyun Kim, Piotr Kokoszka
  • Published Date: Dec 01, 2019

We investigate the asymptotic and finite sample behavior of the Hill estimator applied to time series contaminated by measurement or other errors. We show that for all discrete time models used in...

Space‐efficient estimation of empirical tail dependence coefficients for bivariate data streams Early View

  • Journal: Statistical Analysis and Data Mining: The ASA Data Science Journal
  • Authors: Kaushik Jana, Alastair Gregory
  • Published Date: Nov 21, 2019

Abstract This article proposes a space‐efficient approximation to empirical tail dependence coefficients of an indefinite bivariate stream of data. The approximation, which has stream‐length invariant...

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