We study the computational complexity and variance of multilevel best linear unbiased estimators introduced in [D. Schaden E. Ullmann, SIAM/ASA J. Uncertain. Quantif., 8 (2020), pp. 601--635]. specialize results this work to PDE-based models that are parameterized by a discretization quantity, e.g., finite element mesh size. In particular, we investigate asymptotic so-called sample allocation o...

In this paper, the problem of predicting times to failure of units censored in multiple stages of progressively hybrid censoring for the proportional hazards family is considered. We discuss different classical predictors. The best unbiased predictor ($BUP$), the maximum likelihood predictor ($MLP$) and conditional median predictor ($CMP$) are all derived. As an example, the obtained results ar...

‎In this paper‎, ‎we have dealt with the distribution theory of concomitants of order statistics arising from Farlie-Gumbel-Morgenstern bivariate Lomax distribution‎. ‎We have discussed the estimation of the parameters associated with the distribution of the variable Y of primary interest‎, ‎based on the ranked set sample defined by ordering the marginal observations...

In most on-line learning research the total on-line loss of the algorithm is compared to the total loss of the best o¬-line predictor u from a comparison class of predictors. We call such bounds static bounds. The interesting feature of these bounds is that they hold for an arbitrary sequence of examples. Recently some work has been done where the predictor ut at each trial t is allowed to chan...

In this article, we consider the problem of predicting times to failure of units censored in multiple stages in a progressively censored sample from normal distribution. The best linear unbiased predictor (BLUP), the maximum likelihood predictor (MLP), a modified maximum likelihood predictor (MMLP), an approximate maximum likelihood predictor (AMLP), and the conditional median predictor (CMP) a...