Robust Statistical Inference in Generalized Linear Models Based on Minimum Renyi’s Pseudodistance Estimators

Minimum Renyi’s pseudodistance estimators (MRPEs) enjoy good robustness properties without a significant loss of efficiency in general statistical models, and, in particular, for linear regression models (LRMs). In this line, Castilla et al. considered robust Wald-type test statistics in LRMs based on these MRPEs. In this paper, we extend the theory of MRPEs to Generalized Linear Models (GLMs) using independent and nonidentically distributed observations (INIDO). We derive asymptotic properties of the proposed estimators and analyze their influence function to asses their robustness properties. Additionally, we define robust Wald-type test statistics for testing linear hypothesis and theoretically study their asymptotic distribution, as well as their influence function. The performance of the proposed MRPEs and Wald-type test statistics are empirically examined for the Poisson Regression models through a simulation study, focusing on their robustness properties. We finally test the proposed methods in a real dataset related to the treatment of epilepsy, illustrating the superior performance of the robust MRPEs as well as Wald-type tests.

Advanced non-dimensional dynamic influence function method considering the singularity of the system matrix for accurate eigenvalue analysis of membranes

An advanced non-dimensional dynamic influence function method (NDIF method) for highly accurate free vibration analysis of membranes with arbitrary shapes is proposed in this paper. The existing NDIF method has the weakness of not offering eigenvalues and eigenmodes in the low frequency range when the number of boundary nodes of an analyzed membrane is increased to obtain more accurate result. This paper reveals that the system matrix of the membrane becomes singular in the lower frequency range when the number of the nodes increases excessively. Based on this fact, it provides an efficient way to successfully overcome the weaknesses of the existing NDIF method and still maintain its accuracy. Finally, verification examples show the validity and accuracy of the advanced NDIF method proposed.

Finishing of Micro-Aspheric Tungsten Carbide Mold Using Small Viscoelastic Tool

Tungsten carbide is widely used as the material of replication mold to produce small aspheric optics, and the polishing process determines the precision of the mold. However, for micro-aspheric tungsten carbide mold, the existing polishing methods are difficult to realize the from error modification during the polishing because the polishing tool is always larger than small mold. Therefore, a polishing tool which using polyester fiber cloth to wrap small-size rigid ball is used in this paper. In order to predict the tool influence function (TIF) of this polishing tool, a series of theoretical analysis and experimental verification are carried out in this paper. Firstly, by analyzing the structural and viscoelastic characteristics of the fiber cloth, the pressure distribution in the polishing contact area is determined. And the polishing speed distribution is obtained by analyzing the kinematic movement of the polishing tool; Then, combined with Preston equation, the tool influence function is derived; Afterward, through a series of single point polishing experiments, it is verified that the volume error between the theoretical removal model and the experimental removal is less than 10.8%; Finally, the tool influence function is applied to the form error corrective polishing of small size symmetric aspheric tungsten carbide mold. After one form error corrective polishing, the PV value (Peak to Valley) of form error is decreased from 0.405um to 0.068um, which verifies the effectiveness of the polishing method of small size tungsten carbide mold in form error correction.

Explaining Gold-Mining and Non-Gold Mining Areas' Inequalities in Learning Achievements in Burkina Faso's Primary Education: A Decomposition Analysis

Research shows that learning achievements inequalities exist between students from gold mining areas and those from non-gold mining ones. However, there is no evidence on factors that explain this "new" geographic educational inequality. Exploiting the gold mining boom in Burkina Faso, this study employed re-centered influence function decomposition to explore students' background and school factors which explain these learning achievements inequalities and also estimate the proportion of inequalities explained by unmeasured factors. Findings suggest that, relative to student background factors, most of the learning achievements inequalities between the two types of areas are explained by school factors. Moreover, unmeasured educational factors explain a non-negligible proportion of the inequalities, higher for students on the lower and upper tails of the learning achievements distribution. Suggestions for policymakers are discussed based on the findings of the present study.

Filtered Influence Function of Deformable Mirror for Wavefront Correction in Laser Systems

An influence function filtering method (IFFM) is presented to improve the wavefront correction capability in laser systems by curbing the correction performance degradation resulted from the IF measurement noise. The IFFM is applied to the original measured IF. The resulting filtered IF is then used to calculate the wavefront control signal in each iteration of the closed-loop correction. A theoretical wavefront correction analysis model (CAM) is built. The impact of the IF measurement noise as well as the improvement of the IFFM on the wavefront correction capability are analyzed. A simulation is set up to analyze the wavefront correction capability of the filtered IF using Zernike mode aberrations. An experiment is carried out to study the effectiveness of the IFFM under practical conditions. Simulation and experimental results indicate that the IFFM could effectively reduce the negative effect of the measurement noise and improve the wavefront correction capability in laser systems. The IFFM requires no additional hardware and does not affect the correction speed.

Implication of exosomes derived from cholesterol-accumulated astrocytes in Alzheimer's disease pathology

Amyloid β (Aβ) peptides generated from the amyloid precursor protein (APP) play a critical role in the development of Alzheimer's disease (AD) pathology. Aβ-containing neuronal exosomes, which represent a novel form of intercellular communication, have been shown to influence function/vulnerability of neurons in AD. Unlike neurons, the significance of exosomes derived from astrocytes remains unclear. In this study, we evaluated the significance of exosomes derived from U18666A-induced cholesterol-accumulated astrocytes in the development of AD pathology. Our results show that cholesterol accumulation decreases exosome secretion, whereas lowering cholesterol level increases exosome secretion from cultured astrocytes. Interestingly, exosomes secreted from U18666A-treated astrocytes contain higher levels of APP, APP-CTFs, soluble APP, APP secretases and Aβ1-40 than exosomes secreted from control astrocytes. Furthermore, we show that exosomes derived from U18666A-treated astrocytes can lead to neurodegeneration, which is attenuated by decreasing Aβ production or by neutralizing exosomal Aβ peptide with an Aβ antibody. These results, taken together, suggest that exosomes derived from cholesterol-accumulated astrocytes can play an important role in trafficking APP/Aβ peptides and influencing neuronal viability in the affected regions of the AD brain.