scholarly journals Comparison of estimators of linear time trend in Weibull-distributed low flows

2003 ◽  
Vol 39 (7) ◽  
Author(s):  
Robin T. Clarke
Keyword(s):  
Time Trend ◽  
Linear Time ◽  
Low Flows ◽  
Linear Time Trend

scholarly journals Subsampling Intervals in Autoregressive Models with Linear Time Trend

Econometrica ◽  
2001 ◽  
Vol 69 (5) ◽  
pp. 1283-1314 ◽  
Author(s):  
Joseph P. Romano ◽  
Michael Wolf
Keyword(s):  
Time Trend ◽  
Linear Time ◽  
Autoregressive Models ◽  
Linear Time Trend

scholarly journals Study the Trend Pattern in COVID-19 using Spline-Based Time Series Model: A Bayesian Paradigm

2020 ◽  
Author(s):  
Varun Agiwal ◽  
Jitendra Kumar ◽  
Yau Chun Yip
Keyword(s):  
Time Series ◽  
Time Trend ◽  
Spline Function ◽  
Linear Time ◽  
Linear Trend ◽  
Model Parameters ◽  
Linear Pattern ◽  
Linear Time Trend ◽  
Proposed Model ◽  
Non Linear

A vast majority of the countries is under the economic and health crises due to the current epidemic of coronavirus disease 2019 (COVID-19). The present study analyzes the COVID-19 using time series, which is an essential gizmo for knowing the enlargement of infection and its changing behavior, especially the trending model. We have considered an autoregressive model with a non-linear time trend component that approximately converted into the linear trend using the spline function. The spline function split the COVID-19 series into different piecewise segments between respective knots and fitted the linear time trend. First, we obtain the number of knots with its locations in the COVID-19 series and then the estimation of the best-fitted model parameters are determined under Bayesian setup. The results advocate that the proposed model/methodology is a useful procedure to convert the non-linear time trend into a linear pattern of newly coronavirus case for various countries in the pandemic situation of COVID-19.

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