scholarly journals Model-based estimation of the link between the daily survival probability and a time-varying covariate, application to mosquitofish survival data

2007 ◽  
Vol 210 (2) ◽  
pp. 508-522 ◽  
Author(s):  
Samuel Soubeyrand ◽  
Rémy Beaudouin ◽  
Nicolas Desassis ◽  
Gilles Monod
Keyword(s):  
Survival Data ◽  
Survival Probability ◽  
Time Varying ◽  
Model Based ◽  
Daily Survival

scholarly journals Model-based feed-forward control for time-varying systems with an example for SRF cavities

2020 ◽  
Vol 53 (2) ◽  
pp. 1331-1336
Author(s):  
Sven Pfeiffer ◽  
Annika Eichler ◽  
Holger Schlarb
Keyword(s):  
Time Varying ◽  
Feed Forward ◽  
Feed Forward Control ◽  
Model Based ◽  
Time Varying Systems ◽  
Srf Cavities

A survey on model-based fault diagnosis for linear discrete time-varying systems

2018 ◽  
Vol 306 ◽  
pp. 51-60 ◽  
Author(s):  
Maiying Zhong ◽  
Ting Xue ◽  
Steven X. Ding
Keyword(s):  
Fault Diagnosis ◽  
Discrete Time ◽  
Time Varying ◽  
Model Based ◽  
Time Varying Systems

Oil price risk evaluation using a novel hybrid model based on time-varying long memory

2019 ◽  
Vol 81 ◽  
pp. 70-78 ◽  
Author(s):  
Lu-Tao Zhao ◽  
Kun Liu ◽  
Xin-Lei Duan ◽  
Ming-Fang Li
Keyword(s):  
Hybrid Model ◽  
Long Memory ◽  
Risk Evaluation ◽  
Oil Price ◽  
Price Risk ◽  
Time Varying ◽  
Model Based ◽  
Oil Price Risk

Variable selection for joint models with time-varying coefficients

2019 ◽  
Vol 29 (1) ◽  
pp. 309-322
Author(s):  
Yujing Xie ◽  
Zangdong He ◽  
Wanzhu Tu ◽  
Zhangsheng Yu
Keyword(s):  
Variable Selection ◽  
Survival Data ◽  
Biliary Cirrhosis ◽  
Time Varying ◽  
Joint Models ◽  
Closed Form Solutions ◽  
Varying Coefficients ◽  
Shared Random Effects ◽  
Time Varying Coefficients ◽  
Time Invariant

Many clinical studies collect longitudinal and survival data concurrently. Joint models combining these two types of outcomes through shared random effects are frequently used in practical data analysis. The standard joint models assume that the coefficients for the longitudinal and survival components are time-invariant. In many applications, the assumption is overly restrictive. In this research, we extend the standard joint model to include time-varying coefficients, in both longitudinal and survival components, and we present a data-driven method for variable selection. Specifically, we use a B-spline decomposition and penalized likelihood with adaptive group LASSO to select the relevant independent variables and to distinguish the time-varying and time-invariant effects for the two model components. We use Gaussian-Legendre and Gaussian-Hermite quadratures to approximate the integrals in the absence of closed-form solutions. Simulation studies show good selection and estimation performance. Finally, we use the proposed procedure to analyze data generated by a study of primary biliary cirrhosis.

scholarly journals A Novel Linear Time-Varying GM(1,N) Model for Forecasting Haze: A Case Study of Beijing, China

2019 ◽  
Vol 11 (14) ◽  
pp. 3832 ◽  
Author(s):  
Pingping Xiong ◽  
Jia Shi ◽  
Lingling Pei ◽  
Song Ding
Keyword(s):  
Linear Time ◽  
Dynamic Change ◽  
Time Varying ◽  
The Novel ◽  
Model Based ◽  
Number Sequences ◽  
The Government ◽  
Grey Number ◽  
Beijing China

Haze is the greatest challenge facing China’s sustainable development, and it seriously affects China’s economy, society, ecology and human health. Based on the uncertainty and suddenness of haze, this paper proposes a novel linear time-varying grey model (GM)(1,N) based on interval grey number sequences. Because the original GM(1,N) model based on interval grey number sequences has constant parameters, it neglects the dynamic change characteristics of parameters over time. Therefore, this novel linear time-varying GM(1,N) model, based on interval grey number sequences, is established on the basis of the original GM(1,N) model by introducing a linear time polynomial. To verify the validity and practicability of this model, this paper selects the data of PM10, SO2 and NO2 concentrations in Beijing, China, from 2008 to 2018, to establish a linear time-varying GM(1,3) model based on interval grey number sequences, and the prediction results are compared with the original GM(1,3) model. The result indicates that the prediction effect of the novel model is better than that of the original model. Finally, this model is applied to forecast PM10 concentration for 2019 to 2021 in Beijing, and the forecast is made to provide a reference for the government to carry out haze control.

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