Linear Unbiased Estimation Method Research Articles

A sensor data fusion algorithm based on suboptimal network powered deep learning

To improve the performance of data fusion in wireless sensor networks, based on the suboptimal network powered deep learning model, a data fusion algorithm that combines a stacked autoencoder (SAE) and a clustering protocol is proposed. The fusion algorithm for sensor data is investigated by suboptimal network powered deep learning to analyse the fusion processing at the sensor measurement level and the fusion processing at the localization result level. This paper establishes a suboptimal network powered deep learning model and derives its weighted least squares solution algorithm based on this model, also, the best linear unbiased estimation method for fusion of vibration sensor and vision sensor localization results is adopted. First, to overcome the filter divergence caused by rounding error, reduce the computational complexity, ensure the non-negativity of the covariance, and improve the convergence speed of the filter, the target state volume after fusion using the proposed model is closer to the real state volume of the target compared to the state volume of each local sensor detection, verify the validity of the algorithm was increased.

Mathematical Analysis of a Five Periods Crossover Design for Two Treatments

Introduction: A cross-over design is a repeated measurements design such that each experimental unit receives different treatments during different time periods. Lower order cross-over designs such as the two treatments, two periods and two sequences C (2, 2, 2) design have been discovered to be inefficient and erroneous in their analysis of treatments efficacy. In this regard, higher order cross-over designs have been recommended and developed like: the two treatments, three periods and four sequence C (2, 3, 4) design; and the two treatments, four periods and four sequence C (2, 4, 4) designs. However, there still exists more efficient higher order cross-over designs for two treatments which can be used in bioequivalence experiments. This study gives a new design and analysis for two treatments, five periods and four sequence C (2, 5, 4) cross-over design that gives more precise estimates and provides estimates for intra subject variability. Method: A hypothetical case study was considered on 160 experimental units which are assumed to be randomly selected from a given population. A cross over design of two treatments (A, B) in five periods whose sequences are given by BABAA, ABABB, BAABA and ABBAB were used. Each of the experimental units was used as its own control. The estimates for both direct treatments and treatments carry-over effects were obtained using best linear unbiased estimation method (BLUE). We simulated data for two treatments in five periods and four sequences and used it to test the null hypotheses of no significant differences in both the direct treatments and treatments carry-over effects using the test. The subject profiles plots were used to determine the general trend so as to enable an experimenter make a decision on which of the two treatments under consideration was more efficacious. Results: In testing the null hypothesis of no significant difference in carry-over effects for the two treatments (A&B), the calculated value was found to be 0.55 which was less than the tabular value at 156 degrees of freedom at 95 % confidence level, hence the null hypothesis was not rejected. Similarly, In testing the null hypothesis of no significant difference in treatment effects for A&B, the calculated value was found to be 11.73 which was higher than the tabular value at 156 degrees of freedom at 95% confidence level hence the null hypothesis was rejected, and it was concluded that there was indeed a significant difference in the treatment effects. The mean subject profiles plots for a majority of periods and their respective sequences indicated that the general trend implied that treatment B was more effective as compared to treatment A. Conclusion: In cross-over designs, the presence carry-over effects affect the precision of treatments effects estimates in an experiment. Apart from increasing the washout periods, increasing the number of periods in cross-over designs can help in eliminating the carry-over effects. The C (2, 5, 4) design in this study gives more precise estimates and can provide estimates for intra subject variability. The simulated data indicated that there was significant difference in the treatment effects, and in comparison of the two treatments, treatment B was more effective as compared to treatment A.

Receding horizon estimation for linear discrete-time systems with multi-channel observation delays

This paper investigates the receding horizon state estimation for the linear discrete-time system with multi-channel observation delays. The receding horizon estimation is designed by the reorganized observation technique and the linear unbiased estimation method. The estimation gains are developed by solving a set of Riccati equations, and a stability result about the state estimation is shown. Finally, an example is given to illustrate the efficiency of the receding horizon state estimation.

Moments of order statistics of the standard two-sided power distribution

ABSTRACTThe standard two-sided power distribution is a flexible distribution having uniform, power function and triangular as subdistributions, and it is a reasonable alternative to the Laplace distribution in some cases. In this work, computationally efficient expressions for moments of order statistics, expressions for L-moments, and asymptotic results for sample extrema are derived. Then a simulation study is performed for the location-scale estimation problem of a small data set by considering the maximum likelihood estimation method and the best linear unbiased estimation method based on the moments of order statistics.

Relative Efficiencies of ‘O-BLUE’ Estimators in Simple Linear Regression

Abstract This article is concerned with the estimators of the parameters of the linear model . The estimation procedure consists of using the Best Linear Unbiased Estimation method applied to ordered samples. These are denoted as “O-BLUE” estimators. The exact efficiences of such linear estimators based on censored samples relative to those based upon complete samples are given for some symmetric distributions as well as some scale contaminated distributions. Further, some percentage relative efficiencies of the estimators are given when normal assumptions are made, but do not hold due to specific scale contamination.