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Thread: interval censored data using bootstrap method

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    interval censored data using bootstrap method



    I have time Xi that is interval censored and the data consist of time intervals Ii = [Li, Ri] such that Xi is within Ii , Ri>Li, i=1,...,96
    My interest is theta=E(Xi)
    But I'm not sure what E(Xi) is.
    How should I estimate E(Xi )and conduct inference for it?

    Thanks

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    Re: interval censored data using bootstrap method

    If you already estimate the empirical cumulative distribution function \hat{F}(x),
    or equivalently the empirical survival function \hat{S}(x) = 1 - \hat{F}(x),

    then \hat{E}(x) = \int_{-\infty}^0 \hat{F}(x)dx
+ \int_0^{+\infty} 1 - \hat{F}(x)dx

    The asymptotic approximate standard error of the estimate is available
    (at least for the common Kaplan-Meier case)

    But if you have only interval censored data, without any observed failures,
    traditionally you do not have a unique maximum likelihood nonparametric
    estimates. You may search for the Turnbull's interval/algorithm as well.
    (I am not an expert in survival analysis)

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    Re: interval censored data using bootstrap method


    If I simply assume X~Unif[I], recall I =[L,R]
    and I want to use a kernel density (1/h)*K( ( E(X|I)-x ) / h ) and assume kernel to be Gaussian.
    For E(Xi|Ii), do I simply use the mean of Unif[Li,Ri] so my data becomes (Li+Ri)/2 for each i?

    Thank you!

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