Polynomial spline approach for variable selection and estimation in varying coefficient models for time series data

2015 ◽  
Vol 96 ◽  
pp. 21-27
Author(s):  
Peng Lai ◽  
Jie Meng ◽  
Heng Lian
Keyword(s):  
Time Series ◽  
Variable Selection ◽  
Time Series Data ◽  
Polynomial Spline ◽  
Series Data ◽  
Varying Coefficient Models ◽  
Varying Coefficient ◽  
Variable Selection And Estimation

BAYESIAN 3-DIMENSIONAL SPATIAL VARIABLE SELECTION MODELING OF VOXEL-SPECIFIC HRFs FOR LOCALIZATION IN fMRI TIME SERIES DATA

2018 ◽  
Vol 51 (6) ◽  
pp. 397-426
Author(s):  
Nasrin Borumandnia ◽  
Hamid Alavi Majd ◽  
Farid Zayeri ◽  
Ahmad Reza Baghestani ◽  
Mahmood Reza Gohari ◽  
...  
Keyword(s):  
Time Series ◽  
Variable Selection ◽  
Time Series Data ◽  
Spatial Variable ◽  
Series Data ◽  
3 Dimensional ◽  
Fmri Time Series

scholarly journals Model and Variable Selection Procedures for Semiparametric Time Series Regression

2009 ◽  
Vol 2009 ◽  
pp. 1-37 ◽  
Author(s):  
Risa Kato ◽  
Takayuki Shiohama
Keyword(s):  
Time Series ◽  
Model Selection ◽  
Variable Selection ◽  
Time Series Data ◽  
Selection Procedure ◽  
Semiparametric Regression ◽  
Information Criteria ◽  
Series Data ◽  
Unknown Parameters ◽  
Time Series Regression

Semiparametric regression models are very useful for time series analysis. They facilitate the detection of features resulting from external interventions. The complexity of semiparametric models poses new challenges for issues of nonparametric and parametric inference and model selection that frequently arise from time series data analysis. In this paper, we propose penalized least squares estimators which can simultaneously select significant variables and estimate unknown parameters. An innovative class of variable selection procedure is proposed to select significant variables and basis functions in a semiparametric model. The asymptotic normality of the resulting estimators is established. Information criteria for model selection are also proposed. We illustrate the effectiveness of the proposed procedures with numerical simulations.

Graphical Exploratory Data Analysis for Categorical Longitudinal and Time Series Data

2013 ◽  
Author(s):  
Stephen J. Tueller ◽  
Richard A. Van Dorn ◽  
Georgiy Bobashev ◽  
Barry Eggleston
Keyword(s):  
Time Series ◽  
Data Analysis ◽  
Exploratory Data Analysis ◽  
Time Series Data ◽  
Series Data ◽  
Exploratory Data

Faktor-Faktor Yang Mempengaruhi Nilai Tukar Dollar Amerika Serikat Terhadap Rupiah Tahun 2000–2013

2017 ◽  
Vol 1 (2) ◽  
pp. 177-191
Author(s):  
Rizki Rahma Kusumadewi ◽  
Wahyu Widayat
Keyword(s):  
Time Series ◽  
Exchange Rate ◽  
Money Supply ◽  
Time Series Data ◽  
The United States ◽  
Economic Conditions ◽  
Series Data ◽  
Arch Model ◽  
United States Dollar ◽  
The Exchange Rate

Exchange rate is one tool to measure a country’s economic conditions. The growth of a stable currency value indicates that the country has a relatively good economic conditions or stable. This study has the purpose to analyze the factors that affect the exchange rate of the Indonesian Rupiah against the United States Dollar in the period of 2000-2013. The data used in this study is a secondary data which are time series data, made up of exports, imports, inflation, the BI rate, Gross Domestic Product (GDP), and the money supply (M1) in the quarter base, from first quarter on 2000 to fourth quarter on 2013. Regression model time series data used the ARCH-GARCH with ARCH model selection indicates that the variables that significantly influence the exchange rate are exports, inflation, the central bank rate and the money supply (M1). Whereas import and GDP did not give any influence.

An Anomaly Detection Method with Exemplar Subsequence for Time Series Data

2016 ◽  
Vol 136 (3) ◽  
pp. 363-372
Author(s):  
Takaaki Nakamura ◽  
Makoto Imamura ◽  
Masashi Tatedoko ◽  
Norio Hirai
Keyword(s):  
Time Series ◽  
Anomaly Detection ◽  
Time Series Data ◽  
Detection Method ◽  
Series Data
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