Bootstrapping State-Space Models: Gaussian Maximum Likelihood Estimation and the Kalman Filter

Stoffer, David S.; Wall, Kent D.

doi:10.1080/01621459.1991.10475148

Cited by 121 publications

(94 citation statements)

References 22 publications

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“…We therefore conduct a nonparametric bootstrap procedure to estimate the finite sample variances directly. The applicability of the procedure used in the state-space context has been shown by Stoffer and Wall (1991).…”

Section: Resultsmentioning

confidence: 99%

“…We then sample with replacement T times from {ν t , t = 5 … T} to obtain {ν t ⁎ , t = 5 … T}, a bootstrap sample of innovations. We start at t = 5 because Stoffer and Wall (1991) Table 1 presents the estimation results. 19 For convenience we leave out the results regarding the observations errors, as their standard deviations are estimated to be negligible.…”

Section: Resultsmentioning

confidence: 99%

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Liquidity and credit premia in the yields of highly-rated sovereign bonds

Ejsing

Grothe

2015

Journal of Empirical Finance

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Liquidity and credit premia in the yields of highly-rated sovereign bonds

Ejsing

Grothe

2015

Journal of Empirical Finance

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“…For other literature on resampling methods the readers may wish to access Refs. [65][66][67][68]. The SIS algorithm, another variant of the particle filtering method other than SIR, is a Monte Carlo method which provides the basis for most sequential Monte Carlo (SMC) filters developed recently [56].…”

Section: Particle Filteringmentioning

confidence: 99%

A review on prognostic techniques for non-stationary and non-linear rotating systems

Kan

Tan

Mathew

2015

Mechanical Systems and Signal Processing

305

153

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a b s t r a c tThe field of prognostics has attracted significant interest from the research community in recent times. Prognostics enables the prediction of failures in machines resulting in benefits to plant operators such as shorter downtimes, higher operation reliability, reduced operations and maintenance cost, and more effective maintenance and logistics planning. Prognostic systems have been successfully deployed for the monitoring of relatively simple rotating machines. However, machines and associated systems today are increasingly complex. As such, there is an urgent need to develop prognostic techniques for such complex systems operating in the real world. This review paper focuses on prognostic techniques that can be applied to rotating machinery operating under nonlinear and non-stationary conditions. The general concept of these techniques, the pros and cons of applying these methods, as well as their applications in the research field are discussed. Finally, the opportunities and challenges in implementing prognostic systems and developing effective techniques for monitoring machines operating under nonstationary and non-linear conditions are also discussed.

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“…When sampling is performed at each time interval the process is called systematic resampling, while adaptive resampling refers to the case where resampling is only performed at certain time intervals. A number of different resampling methods can be used [84,89], including the inverse transformation method [83] and the Bootstrap Particle Filter [90][91][92][93]. A detailed discussion on optimal sampling (and resampling) is given in [94].…”

Section: Bayesian Estimation With Kalman Filtersmentioning

confidence: 99%