Industrial Applications of Tesfay Dynamic Regression Model

2021 ◽  
pp. 347-372
Author(s):  
Yohannes Yebabe Tesfay
Keyword(s):  
Regression Model ◽  
Industrial Applications ◽  
Dynamic Regression Model

scholarly journals Identifying the Dynamic Pattern and Influencing Factors of Influenza in Northwest China from 2013 to 2020, Based on Dynamic Regression Model and Wavelet Analysis

2020 ◽  
Author(s):  
Ruonan Wang ◽  
Jiancai Du ◽  
Jiangping Li ◽  
Yajuan Zhang ◽  
Jing Wen ◽  
...  
Keyword(s):  
Wavelet Analysis ◽  
Regression Model ◽  
Influencing Factors ◽  
Minimum Temperature ◽  
Public Awareness ◽  
Arima Model ◽  
Northwest China ◽  
Dynamic Pattern ◽  
Prevention Campaign ◽  
Dynamic Regression Model

Abstract Background: Influenza remains a serious global public health problem and a substantial economic burden. The dynamic pattern of influenza differs considerably among geographic and climatological areas, however, the factors underlying these differences are still uncertain. The aim of this paper is to characterize the dynamic pattern of influenza and its potential influencing factors in Northwest China. Methods: Influenza cases in Ningxia China from Nov. 2013 to Jun. 2020 were served as influenza proxy. Firstly, the baseline seasonal ARIMA model of influenza cases and seasonal pattern were analyzed. Then, the dynamic regression model was used to identifying the potential influencing factors of influenza. In addition, the wavelet analysis was further used to explore the coherence between influenza cases and these significant influencing factors.Results: The high risk periods of influenza in Ningxia presented a winter cycle outbreaks pattern and the fastigium came in January. The seasonal ARIMA(0,0,1)(1,1,0)12 was the optimal baseline forecast model. The dynamic regression models and wavelet analysis indicated that PM2.5 and public awareness are significantly positively associated with influenza, as well as minimum temperature is negatively associated. Conclusion: Meteorological (minimum temperature), pollution (PM2.5) and social (public awareness) factors may significantly associated with influenza in Northwest China. Decreasing PM2.5 concentration or increasing the public awareness prior to the fastigium of influenza may be the serviceable methods to reduce the disease risk of influenza, which have an important implication for policy-makers to choose an optimal time for influenza prevention campaign.

scholarly journals Dynamic Regression and Filtered Data Series: A Laplace Approximation to the Effects of Filtering in Small Samples

1996 ◽  
Vol 12 (3) ◽  
pp. 432-457 ◽  
Author(s):  
Eric Ghysels ◽  
Offer Lieberman
Keyword(s):  
Regression Model ◽  
Quadratic Forms ◽  
Mean Squared Error ◽  
Small Sample ◽  
Data Series ◽  
Finite Sample ◽  
Sample Bias ◽  
Squared Error ◽  
Lagged Dependent Variable ◽  
Dynamic Regression Model

It is common for an applied researcher to use filtered data, like seasonally adjusted series, for instance, to estimate the parameters of a dynamic regression model. In this paper, we study the effect of (linear) filters on the distribution of parameters of a dynamic regression model with a lagged dependent variable and a set of exogenous regressors. So far, only asymptotic results are available. Our main interest is to investigate the effect of filtering on the small sample bias and mean squared error. In general, these results entail a numerical integration of derivatives of the joint moment generating function of two quadratic forms in normal variables. The computation of these integrals is quite involved. However, we take advantage of the Laplace approximations to the bias and mean squared error, which substantially reduce the computational burden, as they yield relatively simple analytic expressions. We obtain analytic formulae for approximating the effect of filtering on the finite sample bias and mean squared error. We evaluate the adequacy of the approximations by comparison with Monte Carlo simulations, using the Census X-11 filter as a specific example

Experimental Evaluation of Identification Schemes on a Direct Drive Robot

Robotica ◽  
1997 ◽  
Vol 15 (5) ◽  
pp. 563-571 ◽  
Author(s):  
Fernando Reyes ◽  
Rafael Kelly
Keyword(s):  
Regression Model ◽  
Experimental Evaluation ◽  
Regression Models ◽  
Degrees Of Freedom ◽  
Dynamic Parameter ◽  
Direct Drive ◽  
Identification Schemes ◽  
Inertial Parameters ◽  
Dynamic Regression Model ◽  
Recursive Parameter Estimator

This paper describes the experimental evaluation of three identification schemes to determine the dynamic parameters of a two degrees of freedom direct-drive robot. These schemes involve a recursive estimator while the regression models are formulated in continuous time. The fact that the total energy of robot manipulators can be represented as a linear relation in the inertial parameters, has motivated the suggestion in the literature of several regression models which are linear in a common dynamic parameter vector. Among them, in this paper we consider the schemes based on the filtered dynamic regression model, the supplied energy regression model and a new one proposed in this paper: the filtered power regression model. The underling recursive parameter estimator used in the experimental evaluation is the standard least-squares.

scholarly journals Time Series Forecasting Using a Two-Level Multi-Objective Genetic Algorithm: A Case Study of Maintenance Cost Data for Tunnel Fans

Algorithms ◽  
2018 ◽  
Vol 11 (8) ◽  
pp. 123 ◽  
Author(s):  
Yamur Al-Douri ◽  
Hussan Hamodi ◽  
Jan Lundberg
Keyword(s):  
Genetic Algorithm ◽  
Time Series ◽  
Regression Model ◽  
Arima Model ◽  
Time Series Forecasting ◽  
Multi Objective ◽  
Multi Objective Genetic Algorithm ◽  
Level 1 ◽  
Dynamic Regression Model ◽  
Level 2

The aim of this study has been to develop a novel two-level multi-objective genetic algorithm (GA) to optimize time series forecasting data for fans used in road tunnels by the Swedish Transport Administration (Trafikverket). Level 1 is for the process of forecasting time series cost data, while level 2 evaluates the forecasting. Level 1 implements either a multi-objective GA based on the ARIMA model or a multi-objective GA based on the dynamic regression model. Level 2 utilises a multi-objective GA based on different forecasting error rates to identify a proper forecasting. Our method is compared with using the ARIMA model only. The results show the drawbacks of time series forecasting using only the ARIMA model. In addition, the results of the two-level model show the drawbacks of forecasting using a multi-objective GA based on the dynamic regression model. A multi-objective GA based on the ARIMA model produces better forecasting results. In level 2, five forecasting accuracy functions help in selecting the best forecasting. Selecting a proper methodology for forecasting is based on the averages of the forecasted data, the historical data, the actual data and the polynomial trends. The forecasted data can be used for life cycle cost (LCC) analysis.

scholarly journals Missing Observations in the Dynamic Regression Model

Econometrica ◽  
1984 ◽  
Vol 52 (6) ◽  
pp. 1415 ◽  
Author(s):  
F. C. Palm ◽  
Th. E. Nijman
Keyword(s):  
Regression Model ◽  
Missing Observations ◽  
Dynamic Regression Model
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