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State-space likelihoods for nonlinear fisheries time-series

https://doi.org/10.1139/f05-116
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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

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Monte Carlo Implementation
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Monte Carlo maximum likelihood estimation for non-Gaussian state space models
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Likelihood Methods in Statistics
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Maximizing Generalized Linear Mixed Model Likelihoods With an Automated Monte Carlo EM Algorithm
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Time Series Analysis of Non-Gaussian Observations Based on State Space Models from Both Classical and Bayesian Perspectives
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COMPARISON OF TWO MODELS AND TWO ESTIMATION METHODS FOR CATCH AND EFFORT DATA
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Constrained Monte Carlo Maximum Likelihood for Dependent Data
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Visualizing catch-age analysis: a case study
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A Robust Method for Parameter Estimation from Catch and Effort Data
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An Errors-In-Variables Bias Model with an Application to Salmon Hatchery Data
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The influence of error on population estimates from catch-age models
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Monte Carlo State-Space Likelihoods by Weighted Posterior Kernel Density Estimation
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FITTING POPULATION MODELS INCORPORATING PROCESS NOISE AND OBSERVATION ERROR
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BETTER INFERENCES FROM POPULATION-DYNAMICS EXPERIMENTS USING MONTE CARLO STATE-SPACE LIKELIHOOD METHODS
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Monte Carlo Statistical Methods
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A Kalman Filter Approach to Catch-at-Length Analysis
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Forecasting, Structural Time Series Models and the Kalman Filter
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Time Series Analysis of Catch-At-Age Observations
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Incoming claims: where this work appears in other papers’ reference lists and arguments.

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