Semiparametric Ridge Regression Approach in Partially Linear Models

Roozbeh, Mahdi; Arashi, Mohammad; Niroumand, Hossein Ali

doi:10.1080/03610910903480818

Cited by 10 publications

(3 citation statements)

References 27 publications

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“…According to [15], ridge regression has a strong relevance in data analysis. They also show that the appropriate solution for collinearity would be not to use ordinary least squares, but to use ridge regression.…”

Section: Regressormentioning

confidence: 99%

Prediction of COVID-19 using Time-Sliding Window: The case of Piauí State - Brazil

Santos

Souza

Lelis

et al. 2021

2020 IEEE International Conference on E-Health Networking, Application &Amp; Services (HEALTHCOM)

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COVID-19 is an infectious disease caused by a type of coronavirus recently discovered, called SARS-CoV-2. It has infected more than 20 million people worldwide and it is responsible for more than 737,000 deaths. This work presents a study that explores linear regression mechanisms combined with a sliding and cumulative time window approach to provide inputs to assist in decision making for public policies, within the scope of the COVID-19 pandemic evolution, whether they are hardening or easing the isolation. Data from five states of Brazil were collected and applied a Ridge regression to predict the curve behavior of cases and deaths of COVID-19. As a result, an Explained Variance Status (EVS) up to 0.998 and 0.999 is presented, considering cases and deaths, respectively. It was concluded that sliding time window bring more information about the infection than cumulative, since public policy changes in a few time-lapse.

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Section: Regressormentioning

confidence: 99%

Prediction of COVID-19 using Time-Sliding Window: The case of Piauí State - Brazil

Santos

Souza

Lelis

et al. 2021

2020 IEEE International Conference on E-Health Networking, Application &Amp; Services (HEALTHCOM)

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“…e statistical model includes linear regression [1], semiparametric and partially varying linear regression [2], nonlinear regression [3], and autoregressive moving average model (ARMA) [4]. e disadvantage of statistical models is that they rely too much on collecting data and estimating parameters.…”

Section: Introductionmentioning

confidence: 99%

A Novel Optimized Nonlinear Grey Bernoulli Model for Forecasting China’s GDP

Zhang

Zheng

2019

Complexity

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The nonlinear grey Bernoulli model, abbreviated as NGBM(1,1), has been successfully applied to control, prediction, and decision-making fields, especially in the prediction of nonlinear small sample time series. However, there are still some problems in improving the prediction accuracy of NGBM(1,1). In this paper, we propose a novel optimized nonlinear grey Bernoulli model for forecasting Chinaʼs GDP. In the new model, the structure and parameters of NGBM(1,1) are optimized simultaneously. Especially, the latest item of first-order accumulative generating operator (1-AGO) sequence is taken as the initial condition, then background value is reconstructed by optimizing weights of neighbor values in 1-AGO sequence, which is based on minimizing the sum of absolute percentage errors, and finally, we establish the new model based on the rolling mechanism. Prediction accuracy of the proposed model is investigated through some simulations and a real example application, and the proposed model is applied to forecast the annual GDP in China from 2019 to 2023.

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“…( 1978 ); Sarkar ( 1992 ); Shi ( 2001 ); Zhong and Yang ( 2007 ); Zhang and Yang ( 2007 ); Tabakan and Akdeniz ( 2010 ); Akdeniz and Tabakan ( 2009 ); Roozbeh et al. ( 2010 ); Duran and Akdeniz ( 2012 ); Duran et al. ( 2012 ); Hu ( 2005 ) and Hu et al.…”

Section: Introductionmentioning

confidence: 99%

Difference-based ridge-type estimator of parameters in restricted partial linear model with correlated errors

2016

SpringerPlus

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In this article, a generalized difference-based ridge estimator is proposed for the vector parameter in a partial linear model when the errors are dependent. It is supposed that some additional linear constraints may hold to the whole parameter space. Its mean-squared error matrix is compared with the generalized restricted difference-based estimator. Finally, the performance of the new estimator is explained by a simulation study and a numerical example.

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Semiparametric Ridge Regression Approach in Partially Linear Models

Cited by 10 publications

References 27 publications

Prediction of COVID-19 using Time-Sliding Window: The case of Piauí State - Brazil

Prediction of COVID-19 using Time-Sliding Window: The case of Piauí State - Brazil

A Novel Optimized Nonlinear Grey Bernoulli Model for Forecasting China’s GDP

Difference-based ridge-type estimator of parameters in restricted partial linear model with correlated errors

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