An additive hazards frailty model with semi-varying coefficients

2021 ◽  
Author(s):  
Zhongwen Zhang ◽  
Xiaoguang Wang ◽  
Yingwei Peng
Keyword(s):  
Frailty Model ◽  
Additive Hazards ◽  
Varying Coefficients

Partially Linear Additive Hazards Regression With Varying Coefficients

2008 ◽  
Vol 103 (483) ◽  
pp. 1200-1213 ◽  
Author(s):  
Guosheng Yin ◽  
Hui Li ◽  
Donglin Zeng
Keyword(s):  
Additive Hazards ◽  
Varying Coefficients ◽  
Partially Linear

Time-varying coefficients in a multivariate frailty model: Application to breast cancer recurrences of several types and death

2015 ◽  
Vol 22 (2) ◽  
pp. 191-215 ◽  
Author(s):  
Yassin Mazroui ◽  
Audrey Mauguen ◽  
Simone Mathoulin-Pélissier ◽  
Gaetan MacGrogan ◽  
Véronique Brouste ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Frailty Model ◽  
Time Varying ◽  
Model Application ◽  
Varying Coefficients ◽  
Time Varying Coefficients

Bayesian Frailty Model for Time to Event Breast Cancer Data

2011 ◽  
Vol 4 (2) ◽  
pp. 8-12
Author(s):  
Leo Alexander T Leo Alexander T ◽  
◽  
Pari Dayal L Pari Dayal L ◽  
Valarmathi S Valarmathi S ◽  
Ponnuraja C Ponnuraja C ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Frailty Model ◽  
Breast Cancer Data ◽  
Time To Event ◽  
Cancer Data

scholarly journals Asymptotic theory for the correlated gamma-frailty model

1998 ◽  
Vol 26 (1) ◽  
pp. 183-214 ◽  
Author(s):  
Erik Parner
Keyword(s):  
Asymptotic Theory ◽  
Frailty Model ◽  
Gamma Frailty

scholarly journals The VIT Transform Approach to Discrete-Time Signals and Linear Time-Varying Systems

Eng ◽  
2021 ◽  
Vol 2 (1) ◽  
pp. 99-125
Author(s):  
Edward W. Kamen
Keyword(s):  
Discrete Time ◽  
Initial Conditions ◽  
Linear Time ◽  
Order Term ◽  
Initial Time ◽  
Time Varying ◽  
Varying Coefficients ◽  
Time Signals ◽  
Time Varying Systems ◽  
Time Varying Coefficients

A transform approach based on a variable initial time (VIT) formulation is developed for discrete-time signals and linear time-varying discrete-time systems or digital filters. The VIT transform is a formal power series in z−1, which converts functions given by linear time-varying difference equations into left polynomial fractions with variable coefficients, and with initial conditions incorporated into the framework. It is shown that the transform satisfies a number of properties that are analogous to those of the ordinary z-transform, and that it is possible to do scaling of z−i by time functions, which results in left-fraction forms for the transform of a large class of functions including sinusoids with general time-varying amplitudes and frequencies. Using the extended right Euclidean algorithm in a skew polynomial ring with time-varying coefficients, it is shown that a sum of left polynomial fractions can be written as a single fraction, which results in linear time-varying recursions for the inverse transform of the combined fraction. The extraction of a first-order term from a given polynomial fraction is carried out in terms of the evaluation of zi at time functions. In the application to linear time-varying systems, it is proved that the VIT transform of the system output is equal to the product of the VIT transform of the input and the VIT transform of the unit-pulse response function. For systems given by a time-varying moving average or an autoregressive model, the transform framework is used to determine the steady-state output response resulting from various signal inputs such as the step and cosine functions.

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