Computational Statistics in Data Science. Группа авторов

interest may be in estimating a quantile of . Let be the distribution function of , assumed to be absolutely continuous with a continuous density . The ‐quantile associated with is

      Sample statistics are used to estimate . That is, let be the th order statistic of . Then, standard arguments for IID sampling and MCMC [11] show that as .

      2.3 Other Estimators

Other quantities of interest that cannot naturally be presented as expectations (i.e., coefficient of variation) can be estimated by standard plug‐in estimation techniques. We focus on estimating the variance–covariance matrix of under

      A natural estimator is the sample covariance matrix

      The strong law of large numbers and the continuous mapping theorem imply that as . For IID samples, is unbiased, but for MCMC samples under stationarity, is typically biased from below [12]

      For MCMC samples, is typically larger than , yielding biased‐from‐below estimation. If obtaining an unbiased estimator of is desirable, a bias correction should be done by estimating Var using methods described in Section 4 .

3 Sampling Distribution

      An asymptotic sampling distribution for estimators in the previous section can be used to summarize the Monte Carlo variability, provided it is available and the limiting variance is estimable. For IID sampling, moment conditions for the function of interest, , with respect to the target distribution, , suffice. For MCMC sampling, more care needs to be taken to ensure that a limiting distribution holds. We present a subset of the conditions under which the estimators exhibit a normal limiting distribution [9, 13]. The main Markov chain assumption is that of polynomial ergodicity. Let denote the total‐variation distance. Let be the ‐step Markov chain transition kernel, and let such that and for ,

      for all