Filters and parameter estimation for a partially observable system subject to random failure with continuous-range observations

2004 ◽  
Vol 36 (4) ◽  
pp. 1212-1230 ◽  
Author(s):  
Daming Lin ◽  
Viliam Makis
Keyword(s):  
Condition Monitoring ◽  
Continuous Time ◽  
The State ◽  
Parameter Estimates ◽  
State Process ◽  
Observation Process ◽  
Special Cases ◽  
Random Failure ◽  
Prediction Problems ◽  
Continuous Range

We consider a failure-prone system operating in continuous time. Condition monitoring is conducted at discrete time epochs. The state of the system is assumed to evolve as a continuous-time Markov process with a finite state space. The observation process with continuous-range values is stochastically related to the state process, which, except for the failure state, is unobservable. Combining the failure information and the condition monitoring information, we derive a general recursive filter, and, as special cases, we obtain recursive formulae for the state estimation and other quantities of interest. Updated parameter estimates are obtained using the expectation-maximization (EM) algorithm. Some practical prediction problems are discussed and finally an illustrative example is given using a real dataset.

Filters and parameter estimation for a partially observable system subject to random failure with continuous-range observations

2004 ◽  
Vol 36 (04) ◽  
pp. 1212-1230 ◽  
Author(s):  
Daming Lin ◽  
Viliam Makis
Keyword(s):  
Condition Monitoring ◽  
Continuous Time ◽  
The State ◽  
Parameter Estimates ◽  
State Process ◽  
Observation Process ◽  
Special Cases ◽  
Random Failure ◽  
Prediction Problems ◽  
Continuous Range

We consider a failure-prone system operating in continuous time. Condition monitoring is conducted at discrete time epochs. The state of the system is assumed to evolve as a continuous-time Markov process with a finite state space. The observation process with continuous-range values is stochastically related to the state process, which, except for the failure state, is unobservable. Combining the failure information and the condition monitoring information, we derive a general recursive filter, and, as special cases, we obtain recursive formulae for the state estimation and other quantities of interest. Updated parameter estimates are obtained using the expectation-maximization (EM) algorithm. Some practical prediction problems are discussed and finally an illustrative example is given using a real dataset.

Recursive filters for a partially observable system subject to random failure

2003 ◽  
Vol 35 (1) ◽  
pp. 207-227 ◽  
Author(s):  
Daming Lin ◽  
Viliam Makis
Keyword(s):  
Condition Monitoring ◽  
Continuous Time ◽  
The State ◽  
Parameter Estimates ◽  
State Process ◽  
Observation Process ◽  
Special Cases ◽  
Random Failure ◽  
Partially Observable System ◽  
Prediction Problems

We consider a failure-prone system which operates in continuous time and is subject to condition monitoring at discrete time epochs. It is assumed that the state of the system evolves as a continuous-time Markov process with a finite state space. The observation process is stochastically related to the state process which is unobservable, except for the failure state. Combining the failure information and the information obtained from condition monitoring, and using the change of measure approach, we derive a general recursive filter, and, as special cases, we obtain recursive formulae for the state estimation and other quantities of interest. Up-dated parameter estimates are obtained using the EM algorithm. Some practical prediction problems are discussed and an illustrative example is given using a real dataset.

Recursive filters for a partially observable system subject to random failure

2003 ◽  
Vol 35 (01) ◽  
pp. 207-227 ◽  
Author(s):  
Daming Lin ◽  
Viliam Makis
Keyword(s):  
Condition Monitoring ◽  
Continuous Time ◽  
The State ◽  
Parameter Estimates ◽  
State Process ◽  
Observation Process ◽  
Special Cases ◽  
Random Failure ◽  
Partially Observable System ◽  
Prediction Problems

We consider a failure-prone system which operates in continuous time and is subject to condition monitoring at discrete time epochs. It is assumed that the state of the system evolves as a continuous-time Markov process with a finite state space. The observation process is stochastically related to the state process which is unobservable, except for the failure state. Combining the failure information and the information obtained from condition monitoring, and using the change of measure approach, we derive a general recursive filter, and, as special cases, we obtain recursive formulae for the state estimation and other quantities of interest. Up-dated parameter estimates are obtained using the EM algorithm. Some practical prediction problems are discussed and an illustrative example is given using a real dataset.

On-line parameter estimation for a failure-prone system subject to condition monitoring

2004 ◽  
Vol 41 (1) ◽  
pp. 211-220 ◽  
Author(s):  
Daming Lin ◽  
Viliam Makis
Keyword(s):  
Parameter Estimation ◽  
Condition Monitoring ◽  
The State ◽  
Homogeneous Markov Process ◽  
Line Parameter ◽  
Failure State ◽  
Finite State ◽  
Random Failure ◽  
On Line ◽  
Partially Observable System

In this paper, we study the on-line parameter estimation problem for a partially observable system subject to deterioration and random failure. The state of the system evolves according to a continuous-time homogeneous Markov process with a finite state space. The state of the system is hidden except for the failure state. When the system is operating, only the information obtained by condition monitoring, which is related to the working state of the system, is available. The condition monitoring observations are assumed to be in continuous range, so that no discretization is required. A recursive maximum likelihood (RML) algorithm is proposed for the on-line parameter estimation of the model. The new RML algorithm proposed in the paper is superior to other RML algorithms in the literature in that no projection is needed and no calculation of the gradient on the surface of the constraint manifolds is required. A numerical example is provided to illustrate the algorithm.

scholarly journals Maximum approximate likelihood estimation of general continuous-time state-space models

2022 ◽  
pp. 1471082X2110657
Author(s):  
Sina Mews ◽  
Roland Langrock ◽  
Marius Ötting ◽  
Houda Yaqine ◽  
Jost Reinecke
Keyword(s):  
State Space ◽  
Continuous Time ◽  
Likelihood Estimation ◽  
The State ◽  
State Space Models ◽  
State Transitions ◽  
Model Parameters ◽  
State Process ◽  
Approximate Likelihood ◽  
Non Gaussian

Continuous-time state-space models (SSMs) are flexible tools for analysing irregularly sampled sequential observations that are driven by an underlying state process. Corresponding applications typically involve restrictive assumptions concerning linearity and Gaussianity to facilitate inference on the model parameters via the Kalman filter. In this contribution, we provide a general continuous-time SSM framework, allowing both the observation and the state process to be non-linear and non-Gaussian. Statistical inference is carried out by maximum approximate likelihood estimation, where multiple numerical integration within the likelihood evaluation is performed via a fine discretization of the state process. The corresponding reframing of the SSM as a continuous-time hidden Markov model, with structured state transitions, enables us to apply the associated efficient algorithms for parameter estimation and state decoding. We illustrate the modelling approach in a case study using data from a longitudinal study on delinquent behaviour of adolescents in Germany, revealing temporal persistence in the deviation of an individual's delinquency level from the population mean.

scholarly journals Numerical Solutions of the Hattendorff Differential Equation for Multi-state Markov Insurance Models

2021 ◽  
Author(s):  
Nathan Ritchey ◽  
Rajeev Rajaram
Keyword(s):  
Differential Equation ◽  
Time Evolution ◽  
Continuous Time ◽  
Numerical Solutions ◽  
Random Variable ◽  
The State ◽  
Numerical Results ◽  
State Process ◽  
Differential Equa

We provide methodology and numerical results for the Hattendorff differential equa- tion for the continuous time evolution of the variance of L(j)t , the loss at time t random variable for a multi-state process, given that the state at time t is j.

scholarly journals Numerical Solutions of the Hattendorff Differential Equation for Multi-state Markov Insurance Models

2021 ◽  
Author(s):  
Nathan Ritchey ◽  
Rajeev Rajaram
Keyword(s):  
Differential Equation ◽  
Time Evolution ◽  
Continuous Time ◽  
Numerical Solutions ◽  
Random Variable ◽  
The State ◽  
Numerical Results ◽  
State Process ◽  
Differential Equa

We provide methodology and numerical results for the Hattendorff differential equa- tion for the continuous time evolution of the variance of L(j)t , the loss at time t random variable for a multi-state process, given that the state at time t is j.

On-line parameter estimation for a failure-prone system subject to condition monitoring

2004 ◽  
Vol 41 (01) ◽  
pp. 211-220 ◽  
Author(s):  
Daming Lin ◽  
Viliam Makis
Keyword(s):  
Parameter Estimation ◽  
Condition Monitoring ◽  
The State ◽  
Homogeneous Markov Process ◽  
Line Parameter ◽  
Failure State ◽  
Finite State ◽  
Random Failure ◽  
On Line ◽  
Partially Observable System

In this paper, we study the on-line parameter estimation problem for a partially observable system subject to deterioration and random failure. The state of the system evolves according to a continuous-time homogeneous Markov process with a finite state space. The state of the system is hidden except for the failure state. When the system is operating, only the information obtained by condition monitoring, which is related to the working state of the system, is available. The condition monitoring observations are assumed to be in continuous range, so that no discretization is required. A recursive maximum likelihood (RML) algorithm is proposed for the on-line parameter estimation of the model. The new RML algorithm proposed in the paper is superior to other RML algorithms in the literature in that no projection is needed and no calculation of the gradient on the surface of the constraint manifolds is required. A numerical example is provided to illustrate the algorithm.

scholarly journals Estimating Dynamic Signals From Trial Data With Censored Values

2017 ◽  
Vol 1 ◽  
pp. 58-81 ◽  
Author(s):  
Ali Yousefi ◽  
Darin D. Dougherty ◽  
Emad N. Eskandar ◽  
Alik S. Widge ◽  
Uri T. Eden
Keyword(s):  
Censored Data ◽  
Latent Variable ◽  
Statistical Power ◽  
Computationally Efficient ◽  
Behavioral Experiments ◽  
Modeling Framework ◽  
State Process ◽  
Observation Process ◽  
Special Cases ◽  
Space Modeling

Censored data occur commonly in trial-structured behavioral experiments and many other forms of longitudinal data. They can lead to severe bias and reduction of statistical power in subsequent analyses. Principled approaches for dealing with censored data, such as data imputation and methods based on the complete data’s likelihood, work well for estimating fixed features of statistical models but have not been extended to dynamic measures, such as serial estimates of an underlying latent variable over time. Here we propose an approach to the censored-data problem for dynamic behavioral signals. We developed a state-space modeling framework with a censored observation process at the trial timescale. We then developed a filter algorithm to compute the posterior distribution of the state process using the available data. We showed that special cases of this framework can incorporate the three most common approaches to censored observations: ignoring trials with censored data, imputing the censored data values, or using the full information available in the data likelihood. Finally, we derived a computationally efficient approximate Gaussian filter that is similar in structure to a Kalman filter, but that efficiently accounts for censored data. We compared the performances of these methods in a simulation study and provide recommendations of approaches to use, based on the expected amount of censored data in an experiment. These new techniques can broadly be applied in many research domains in which censored data interfere with estimation, including survival analysis and other clinical trial applications.

Optimal Consumption and Insurance: A Continuous-time Markov Chain Approach

2008 ◽  
Vol 38 (01) ◽  
pp. 231-257 ◽  
Author(s):  
Holger Kraft ◽  
Mogens Steffensen
Keyword(s):  
Continuous Time ◽  
Optimal Solution ◽  
Decision Problems ◽  
Optimal Consumption ◽  
Continuous Time Markov Chain ◽  
Financial Decision Making ◽  
Markov Chain Approach ◽  
Special Cases ◽  
Financial Decision ◽  
The Individual

Personal financial decision making plays an important role in modern finance. Decision problems about consumption and insurance are in this article modelled in a continuous-time multi-state Markovian framework. The optimal solution is derived and studied. The model, the problem, and its solution are exemplified by two special cases: In one model the individual takes optimal positions against the risk of dying; in another model the individual takes optimal positions against the risk of losing income as a consequence of disability or unemployment.

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