Continuous Time Markov Models for Binary Longitudinal Data

2006 ◽  
Vol 48 (3) ◽  
pp. 411-419 ◽  
Author(s):  
Richard H. Jones ◽  
Stanley Xu ◽  
Gary K. Grunwald
Keyword(s):  
Longitudinal Data ◽  
Continuous Time ◽  
Markov Models ◽  
Binary Longitudinal Data

Using Longitudinal Data to Model Teachers' Ratings of Classroom Behavior as a Dynamic Process

1981 ◽  
Vol 6 (3) ◽  
pp. 237-255 ◽  
Author(s):  
Ian Plewis
Keyword(s):  
Measurement Error ◽  
Longitudinal Data ◽  
Dynamic Process ◽  
Categorical Data ◽  
Continuous Time ◽  
Classroom Behavior ◽  
Markov Models ◽  
Time Measurement ◽  
Small Sample ◽  
Educational Implications

Simple Markov models are fitted to a small sample of longitudinal categorical data of teachers' ratings of children's classroom behavior. Although the data consist only of observations at 5 occasions, it was possible, after dividing the data into two groups, to fit plausible models in continuous time. Measurement error and alternative longitudinal designs are discussed, and some possible educational implications are noted.

The analysis of binary longitudinal data with time-dependent covariates

2012 ◽  
Vol 31 (10) ◽  
pp. 931-948 ◽  
Author(s):  
Matthew W. Guerra ◽  
Justine Shults ◽  
Jay Amsterdam ◽  
Thomas Ten-Have
Keyword(s):  
Longitudinal Data ◽  
Time Dependent ◽  
Binary Longitudinal Data ◽  
Time Dependent Covariates

scholarly journals Pricing and risk management under multivariate switching models

2021 ◽  
Author(s):  
Atousa Assadihaghi
Keyword(s):  
Monte Carlo ◽  
Continuous Time ◽  
Value At Risk ◽  
Markov Models ◽  
Risk Measures ◽  
Occupation Times ◽  
Taylor Expansions ◽  
Switching Regime ◽  
Switching Models ◽  
Analytic Expressions

The objective of this thesis is to provide a simulations-free approximation to the price of multivariate derivatives and for the calculation of risk measures like Value at Risk (VaR). The first chapters are dedicated to the pricing of multivariate derivatives. In particular we focus on multivariate derivatives under switching regime Markov models. We consider the cases of two and three states of the switching regime Markov model, and derive analytic expressions for the first and second order moments of the occupation times of the continuous-time Markov process. Then we use these expressions to provide approximations for the derivative prices based on Taylor expansions. We compare our closed form approximations with Monte Carlo simulations. In the last chapter we also provide a simulations-free approximation for the VaR under a switching regime model with two states. We compare these VaR estimations with those obtained using Monte Carlo.

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