We propose a fast penalized spline method for bivariate smoothing. Univariate Pspline smoothers Eilers and Marx (1996) are applied simultaneously along both coordinates. The new smoother has a sandwich form which suggested the name “sandwich smoother” to a referee. The sandwich smoother has a tensor product structure that simplifies an asymptotic analysis and it can be fast computed. We derive ...

This paper focuses on the empirical autocovariance operator of H-valued periodically correlated processes. It will be demonstrated that the empirical estimator converges to a limit with the same periodicity as the main process. Moreover, the rate of convergence of the empirical autocovariance operator in Hilbert-Schmidt norm is derived.

BACKGROUND Previous studies have proposed a simple product-based estimator for calculating exposure-specific risks (ESR), but the methodology has not been rigorously evaluated. The goal of our study was to evaluate the existing methodology for calculating the ESR, propose an improved point estimator, and propose variance estimates that will allow the calculation of confidence intervals (CIs). ...

Simultaneous estimation of system and components reliability is considered when independent partition-based Dirichlet(PBD) prior is assigned on components distribution. Denote the lifetime of component j in the i-th system by {Tij , j = 1, 2, 3, . . . ,K} and the end of monitoring time by {τi, i = 1, 2, . . . , n}. Assume that {Tij , i = 1, 2, 3, . . . , n} and {τi, i = 1, 2, . . . , n} are IID...

This paper deals with the problem of estimation of population mean in two-phase sampling. A ratio-product estimator of population mean using known coefficient of kurtosis of two auxiliary variates has been proposed. In fact, it is a two-phase sampling version of Tailor et al. (2010) estimator and its properties are studied. Proposed estimator has been compared with usual unbiased estimator, cla...

Barndorff-Nielsen and Cox (1994, p.319) modify an estimative prediction limit to obtain an improved prediction limit with better coverage properties. Kabaila and Syuhada (2008) present a simulation-based approximation to this improved prediction limit, which avoids the extensive algebraic manipulations required for this modification. We present a modification of an estimative prediction interva...

Consider the problem of joint parameter estimation and prediction in a Markov random field: i.e., the model parameters are estimated on the basis of an initial set of data, and then the fitted model is used to perform prediction (e.g., smoothing, denoising, interpolation) on a new noisy observation. Working in the computation-limited setting, we analyze a joint method in which the same convex v...

Consider the problem of joint parameter estimation and prediction in a Markov random field: i.e., the model parameters are estimated on the basis of an initial set of data, and then the fitted model is used to perform prediction (e.g., smoothing, denoising, interpolation) on a new noisy observation. Working under the restriction of limited computation, we analyze a joint method in which the sam...

Using the well-known product-limit form of the Kaplan-Meier estimator from statistics, we propose a new class of nonparametric adaptive data-driven policies for stochastic inventory control problems. We focus on the distribution-free newsvendor model with censored demands. The assumption is that the demand distribution is not known and there are only sales data available. We study the theoretic...

Consider the problem of joint parameter estimation and prediction in a Markov random field: that is, the model parameters are estimated on the basis of an initial set of data, and then the fitted model is used to perform prediction (e.g., smoothing, denoising, interpolation) on a new noisy observation. Working under the restriction of limited computation, we analyze a joint method in which the ...