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• W2949079764 abstract "Abstract. Parameter estimation in diffusion processes from discrete observations up to a first-passage time is clearly of practical relevance, but does not seem to have been studied so far. In neuroscience, many models for the membrane potential evolution involve the presence of an upper threshold. Data are modelled as discretely observed diffusions which are killed when the threshold is reached. Statistical inference is often based on a misspecified likelihood ignoring the presence of the threshold causing severe bias, e.g. the bias incurred in the drift parameters of the Ornstein–Uhlenbeck model for biological relevant parameters can be up to 25–100 per cent. We compute or approximate the likelihood function of the killed process. When estimating from a single trajectory, considerable bias may still be present, and the distribution of the estimates can be heavily skewed and with a huge variance. Parametric bootstrap is effective in correcting the bias. Standard asymptotic results do not apply, but consistency and asymptotic normality may be recovered when multiple trajectories are observed, if the mean first-passage time through the threshold is finite. Numerical examples illustrate the results and an experimental data set of intracellular recordings of the membrane potential of a motoneuron is analysed." @default.
• W2949079764 created "2019-06-27" @default.
• W2949079764 creator A5043869410 @default.
• W2949079764 creator A5066654049 @default.
• W2949079764 date "2012-08-29" @default.
• W2949079764 modified "2023-10-16" @default.
• W2949079764 title "Estimation in Discretely Observed Diffusions Killed at a Threshold" @default.
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• W2949079764 doi "https://doi.org/10.1111/j.1467-9469.2012.00810.x" @default.
• W2949079764 hasPublicationYear "2012" @default.
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