Robust inference and modelling for the single ion channel

DOI10.1080/00949650412331272886zbMath1067.62109OpenAlexW1994673979MaRDI QIDQ5312747

Peter L. McKinnon, Brenton R. Clarke

Publication date: 25 August 2005

Published in: Journal of Statistical Computation and Simulation (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/00949650412331272886

zbMATH Keywords

EM algorithmminimum distance estimationMarkov chainion channelsM-estimatormixtures of exponential distributions

Mathematics Subject Classification ID

Applications of statistics to biology and medical sciences; meta analysis (62P10) Robustness and adaptive procedures (parametric inference) (62F35) Markov processes: estimation; hidden Markov models (62M05) Cell biology (92C37)

Related Items (4)

On the convergence of Newton's method when estimating higher dimensional parameters ⋮ A fast robust method for fitting gamma distributions ⋮ Procedure to identify outliers through cumulative distribution of extremes in a Gamma response model ⋮ Exploring voltage-dependent ion channels in silico by hysteretic conductance

Cites Work

• Graphs, random sums, and sojourn time distributions, with application to ion-channel modeling
• An unbiased minimum distance estimator of the proportion parameter in a mixture of two normal distributions
• Robust estimation of \(k\)-component univariate normal mixtures
• Minimum distance estimators
• Stochastic models for ion channels: Introduction and bibliography
• Minimum mean squared estimation of location and scale parameters under misspecification of the model
• Statistical Analysis of Ion Channel Data Using Hidden Markov Models With Correlated State-Dependent Noise and Filtering
• Bayesian inference for ion–channel gating mechanisms directly from single–channel recordings, using Markov chain Monte Carlo
• A cautionary note on likelihood ratio tests in mixture models
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