we develop a two phase sampling procedure to determine the sample size necessary to estimatethe population mean of a normally distributed random variable and show that the resulting estimator has preassigned variance and is unbiased under a regular condition. we present a necessary and sufficient condition under which the final sample mean is an unbiased estimator for the population mean.

Objective: This paper aims to introduce a modified kernel-type ridge estimator for partially linear models under randomly-right censored data. Such models include two main issues that need to be solved: multi-collinearity and censorship. To address these issues, we improved the kernel estimator based on synthetic data transformation and kNN imputation techniques. The key idea of this paper is t...

Matrix data sets arise in network analysis for medical applications, where each belongs to a subject and represents measurable phenotype. These large dimensional are often modeled using lower-dimensional latent variables, which explain most of the observed variability can be used predictive purposes. In this paper, we provide asymptotic convergence guarantees estimation hierarchical statistical...

Coherent symbol by symbol detection using fractionally spaced samples in a time-varying frequency at Rayleigh fading channel is presented. The proposed detector incorporates a decision feedback (DF) maximum a posteriori (MAP) channel estimator that can be applied to a timevarying at Rayleigh fading channel with an arbitrary but known spaced-time correlation function. The best achievable bit err...

In this paper, we propose an efficient method to estimate the Weingarten map for point cloud data sampled from manifold embedded in Euclidean space. A statistical model is established analyze asymptotic property of estimator. particular, show convergence rate as sample size tends infinity. We verify through simulated and apply estimated curvature estimation simplification multiple real sets.

This paper proposes a robust navigation method for Autonomous Underwater Vehicles (AUVs) operating around structures such as harbors and breakwaters. This method consists of a state estimator and a motion controller, enabling localization in the configuration map as well as following pre-given waypoints without any external help. The state estimator is based on a “particle filter” where the veh...

Motivated by the continuing interest in discrete time hidden Markov models (HMMs), this paper reexamines these models using a risk-based approach. Simple modifications of the classical optimization criteria for hidden path inference lead to a new class of hidden path estimators. The estimators are efficiently computed in the usual forward-backward manner and a corresponding dynamic programming ...