Optimal dividend payment under a ruin constraint: Discrete time and state space

2003 ◽  
Vol 26 (2) ◽  
pp. 255-264 ◽  
Author(s):  
Christian Hipp
Keyword(s):  
State Space ◽  
Discrete Time ◽  
Dividend Payment ◽  
Optimal Dividend

scholarly journals State space realizations robust to overloading for discrete-time LTI systems

2019 ◽  
Vol 156 ◽  
pp. 12-20 ◽  
Author(s):  
Shuichi Ohno ◽  
Yuichi Yoshimura
Keyword(s):  
State Space ◽  
Discrete Time

Criteria for the non-ergodicity of stochastic processes: application to the exponential back-off protocol

1987 ◽  
Vol 24 (02) ◽  
pp. 347-354 ◽  
Author(s):  
Guy Fayolle ◽  
Rudolph Iasnogorodski
Keyword(s):  
Stochastic Process ◽  
Markov Chain ◽  
Stochastic Processes ◽  
State Space ◽  
Discrete Time ◽  
Random Variables ◽  
Countable State Space ◽  
Countable State ◽  
New Criteria

In this paper, we present some simple new criteria for the non-ergodicity of a stochastic process (Yn ), n ≧ 0 in discrete time, when either the upward or downward jumps are majorized by i.i.d. random variables. This situation is encountered in many practical situations, where the (Yn ) are functionals of some Markov chain with countable state space. An application to the exponential back-off protocol is described.

scholarly journals Discrete-time autoregressive model for unequally spaced time-series observations

2019 ◽  
Vol 627 ◽  
pp. A120 ◽  
Author(s):  
Felipe Elorrieta ◽  
Susana Eyheramendy ◽  
Wilfredo Palma
Keyword(s):  
Time Series ◽  
State Space ◽  
Discrete Time ◽  
Likelihood Estimation ◽  
Variable Stars ◽  
Space Representation ◽  
Finite Sample ◽  
Unequally Spaced ◽  
Astronomical Time ◽  
Astronomical Time Series

Most time-series models assume that the data come from observations that are equally spaced in time. However, this assumption does not hold in many diverse scientific fields, such as astronomy, finance, and climatology, among others. There are some techniques that fit unequally spaced time series, such as the continuous-time autoregressive moving average (CARMA) processes. These models are defined as the solution of a stochastic differential equation. It is not uncommon in astronomical time series, that the time gaps between observations are large. Therefore, an alternative suitable approach to modeling astronomical time series with large gaps between observations should be based on the solution of a difference equation of a discrete process. In this work we propose a novel model to fit irregular time series called the complex irregular autoregressive (CIAR) model that is represented directly as a discrete-time process. We show that the model is weakly stationary and that it can be represented as a state-space system, allowing efficient maximum likelihood estimation based on the Kalman recursions. Furthermore, we show via Monte Carlo simulations that the finite sample performance of the parameter estimation is accurate. The proposed methodology is applied to light curves from periodic variable stars, illustrating how the model can be implemented to detect poor adjustment of the harmonic model. This can occur when the period has not been accurately estimated or when the variable stars are multiperiodic. Last, we show how the CIAR model, through its state space representation, allows unobserved measurements to be forecast.

scholarly journals Robust estimation for discrete‐time state space models

2020 ◽  
Author(s):  
William H. Aeberhard ◽  
Eva Cantoni ◽  
Chris Field ◽  
Hans R. Künsch ◽  
Joanna Mills Flemming ◽  
...  
Keyword(s):  
State Space ◽  
Robust Estimation ◽  
Discrete Time ◽  
State Space Models
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