Rota and Laitila (2015) suggest an alternative two-step calibration estimation resulting from combining two approaches, i.e., linear (Särndal Lundström, 2005) propensity score (Chang Kott, 2008), when the functional form of response probability is assumed to be known. The first step focuses on estimating this function second total a survey variable. This paper extends these previous findings by...

In this paper, an estimation of the Gaussian noise variance based on observed (measured) maximums of subsets of samples is given. Circumstances of the measurement environment being limited, only maximums of subsets of samples are available and the non-constant variance of the Gaussian noise can be estimated. In the case of power line noise, the variance of the zero mean Gaussian noise is a peri...

For statistical inference of means of stationary processes, one needs to estimate their time-average variance constants (TAVC) or long-run variances. For a stationary process, its TAVC is the sum of all its covariances and it is a multiple of the spectral density at zero. The classical TAVC estimate which is based on batched means does not allow recursive updates and the required memory complex...

Recent advances in policy gradient methods and deep learning have demonstrated their applicability for complex reinforcement learning problems. However, the variance of the performance gradient estimates obtained from the simulation is often excessive, leading to poor sample efficiency. In this paper, we apply the stochastic variance reduced gradient descent (SVRG) technique [1] to model-free p...

In this paper, we address the problem of deriving bounds for the moments of nearest neighbor distributions. The bounds are formulated for the general case and specifically applied to the problem of noise variance estimation with the Delta test and the Gamma test. For this problem, we focus on the rate of convergence and the bias of the estimators and validate the theoretical achievements with e...

The aim of the paper is to derive the numerical least-squares esti-mator for mean and variance of random variable. In order to do so the following questions have to be answered: (i) what is the statistical model for the estimation procedure? (ii) what are the properties of the estimator, like optimality (in which class) or asymptotic properties? (iii) how does the estimator work in practice, ho...