scholarly journals Finite sample theory for high-dimensional functional/scalar time series with applications

2022 ◽  
Vol 16 (1) ◽  
Author(s):  
Qin Fang ◽  
Shaojun Guo ◽  
Xinghao Qiao
Keyword(s):  
Time Series ◽  
High Dimensional ◽  
Finite Sample ◽  
Scalar Time Series

scholarly journals Dynamic Factor Copula Models with Estimated Cluster Assignments

2021 ◽  
Vol 2021 (026) ◽  
pp. 1-52
Author(s):  
Dong Hwan Oh ◽  
◽  
Andrew J. Patton ◽  
Keyword(s):  
Time Series ◽  
Clustering Algorithm ◽  
Ad Hoc ◽  
High Dimensional ◽  
Dynamic Factor ◽  
Finite Sample ◽  
Copula Models ◽  
Finite Sample Properties ◽  
Out Of Sample ◽  
Individual Variables

This paper proposes a dynamic multi-factor copula for use in high dimensional time series applications. A novel feature of our model is that the assignment of individual variables to groups is estimated from the data, rather than being pre-assigned using SIC industry codes, market capitalization ranks, or other ad hoc methods. We adapt the k-means clustering algorithm for use in our application and show that it has excellent finite-sample properties. Applying the new model to returns on 110 US equities, we find around 20 clusters to be optimal. In out-of-sample forecasts, we find that a model with as few as five estimated clusters significantly outperforms an otherwise identical model with 21 clusters formed using two-digit SIC codes.

scholarly journals Mean Empirical Likelihood Inference for Response Mean with Data Missing at Random

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Hanji He ◽  
Guangming Deng
Keyword(s):  
Empirical Likelihood ◽  
Missing At Random ◽  
Confidence Regions ◽  
Likelihood Inference ◽  
High Dimensional ◽  
Finite Sample ◽  
The Mean ◽  
Data Missing ◽  
Consistency And Asymptotic Normality ◽  
The Impact

We extend the mean empirical likelihood inference for response mean with data missing at random. The empirical likelihood ratio confidence regions are poor when the response is missing at random, especially when the covariate is high-dimensional and the sample size is small. Hence, we develop three bias-corrected mean empirical likelihood approaches to obtain efficient inference for response mean. As to three bias-corrected estimating equations, we get a new set by producing a pairwise-mean dataset. The method can increase the size of the sample for estimation and reduce the impact of the dimensional curse. Consistency and asymptotic normality of the maximum mean empirical likelihood estimators are established. The finite sample performance of the proposed estimators is presented through simulation, and an application to the Boston Housing dataset is shown.

scholarly journals QUANTILE DOUBLE AUTOREGRESSION

2021 ◽  
pp. 1-47
Author(s):  
Qianqian Zhu ◽  
Guodong Li
Keyword(s):  
Time Series ◽  
Financial Time Series ◽  
Simulation Studies ◽  
Finite Sample ◽  
Conditional Quantile ◽  
New Model ◽  
Conditional Quantiles ◽  
Financial Time ◽  
Portmanteau Tests ◽  
Strict Stationarity

Many financial time series have varying structures at different quantile levels, and also exhibit the phenomenon of conditional heteroskedasticity at the same time. However, there is presently no time series model that accommodates both of these features. This paper fills the gap by proposing a novel conditional heteroskedastic model called “quantile double autoregression”. The strict stationarity of the new model is derived, and self-weighted conditional quantile estimation is suggested. Two promising properties of the original double autoregressive model are shown to be preserved. Based on the quantile autocorrelation function and self-weighting concept, three portmanteau tests are constructed to check the adequacy of the fitted conditional quantiles. The finite sample performance of the proposed inferential tools is examined by simulation studies, and the need for use of the new model is further demonstrated by analyzing the S&P500 Index.

Weak Signal Detection in Underwater Acoustic Based on Time-Series Characteristic

2011 ◽  
Vol 216 ◽  
pp. 548-552
Author(s):  
Hai Ping Wu ◽  
Shi Jian Zhu ◽  
Jing Jun Lou ◽  
Li Yang Yu
Keyword(s):  
Time Series ◽  
Signal Detection ◽  
Matched Filter ◽  
Doppler Frequency ◽  
High Dimensional ◽  
Filter Method ◽  
Weak Signal ◽  
Weak Signal Detection ◽  
Underwater Acoustic ◽  
Radar Echo

For limitation of the matched filter method in underwater acoustic detection,a method of underwater acoustic weak signal detection based on time series characteristic quantity is proposed．Chaotic waveforms, which have thumbtack type ambiguity function, is selected as the waveform of active sonar in the situation of High Dynamic Doppler Frequency Shift. According to the change of correlation dimension while chaotic radar echo appears in the chaotic background, chaotic radar echo is checked out by the means of simulation in the situation of high dimensional chaotic background and low dimensional chaotic background．The method proves out in high dimensional chaotic background．

scholarly journals An autoregressive model for irregular time series of variable stars

2016 ◽  
Vol 12 (S325) ◽  
pp. 259-262
Author(s):  
Susana Eyheramendy ◽  
Felipe Elorrieta ◽  
Wilfredo Palma
Keyword(s):  
Time Series ◽  
Gravitational Lensing ◽  
Autoregressive Model ◽  
Likelihood Estimation ◽  
Estimation Procedure ◽  
Variable Stars ◽  
Light Curves ◽  
Finite Sample ◽  
Harmonic Model ◽  
Dependency Structure

AbstractThis paper discusses an autoregressive model for the analysis of irregularly observed time series. The properties of this model are studied and a maximum likelihood estimation procedure is proposed. The finite sample performance of this estimator is assessed by Monte Carlo simulations, showing accurate estimators. We implement this model to the residuals after fitting an harmonic model to light-curves from periodic variable stars from the Optical Gravitational Lensing Experiment (OGLE) and Hipparcos surveys, showing that the model can identify time dependency structure that remains in the residuals when, for example, the period of the light-curves was not properly estimated.

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