Hierarchical Time-Varying Estimation of Asset Pricing Models

This paper presents a new hierarchical methodology for estimating multi factor dynamic asset pricing models. The approach is loosely based on the sequential Fama–MacBeth approach and developed in a kernel regression framework. However, the methodology uses a very flexible bandwidth selection method which is able to emphasize recent data and information to derive the most appropriate estimates of risk premia and factor loadings at each point in time. The choice of bandwidths and weighting schemes are achieved by a cross-validation procedure; this leads to consistent estimators of the risk premia and factor loadings. Additionally, an out-of-sample forecasting exercise indicates that the hierarchical method leads to a statistically significant improvement in forecast loss function measures, independently of the type of factor considered.

Active Magnetoelectric Motion Sensing: Examining Performance Metrics with an Experimental Setup

Magnetoelectric (ME) sensors with a form factor of a few millimeters offer a comparatively low magnetic noise density of a few pT/Hz in a narrow frequency band near the first bending mode. While a high resonance frequency (kHz range) and limited bandwidth present a challenge to biomagnetic measurements, they can potentially be exploited in indirect sensing of non-magnetic quantities, where artificial magnetic sources are applicable. In this paper, we present the novel concept of an active magnetic motion sensing system optimized for ME sensors. Based on the signal chain, we investigated and quantified key drivers of the signal-to-noise ratio (SNR), which is closely related to sensor noise and bandwidth. These considerations were demonstrated by corresponding measurements in a simplified one-dimensional motion setup. Accordingly, we introduced a customized filter structure that enables a flexible bandwidth selection as well as a frequency-based separation of multiple artificial sources. Both design goals target the prospective application of ME sensors in medical movement analysis, where a multitude of distributed sensors and sources might be applied.

A new method for structural safety and reliability analysis of offshore wind turbines

With the motivation to overcome the shortcomings of the Rosenblatt Inverse-First-Order Reliability environmental contour method, in this study, the use of bivariate kernel density estimation with smoothed cross-validation bandwidth selection method is proposed for generating more accurate environmental contour lines. The environmental contour lines at a chosen offshore site obtained by using the proposed new method were compared with those obtained by using the Rosenblatt Inverse-First-Order Reliability environmental contour method, and the accuracy and effectiveness of the proposed new method have been fully and clearly substantiated. Next, the 50-year extreme structural dynamic responses of a monopile-supported 5MW offshore wind turbine installed at this chosen offshore site based on the proposed new method and the Rosenblatt Inverse-First-Order Reliability environmental contour approach were calculated. Analyzing the calculating results, it can be found that the 50-year extreme fore-aft shear force value based on the 50-year extreme sea state obtained using the proposed new method is 78.9% larger than the corresponding value obtained based on the Rosenblatt Inverse-First-Order Reliability contour method. The calculation results in this paper were further systematically analyzed and compared, and the necessity and importance of using more realistic environmental contour lines (such as those generated using the proposed new method) have been finally highlighted.

lme4GS: An R-Package for Genomic Selection

Genomic selection (GS) is a technology used for genetic improvement, and it has many advantages over phenotype-based selection. There are several statistical models that adequately approach the statistical challenges in GS, such as in linear mixed models (LMMs). An active area of research is the development of software for fitting LMMs mainly used to make genome-based predictions. The lme4 is the standard package for fitting linear and generalized LMMs in the R-package, but its use for genetic analysis is limited because it does not allow the correlation between individuals or groups of individuals to be defined. This article describes the new lme4GS package for R, which is focused on fitting LMMs with covariance structures defined by the user, bandwidth selection, and genomic prediction. The new package is focused on genomic prediction of the models used in GS and can fit LMMs using different variance–covariance matrices. Several examples of GS models are presented using this package as well as the analysis using real data.

Bandwidth Selection for Kernel Generalized Regression Neural Networks in Identification of Hammerstein Systems

This paper addresses the issue of data-driven smoothing parameter (bandwidth) selection in the context of nonparametric system identification of dynamic systems. In particular, we examine the identification problem of the block-oriented Hammerstein cascade system. A class of kernel-type Generalized Regression Neural Networks (GRNN) is employed as the identification algorithm. The statistical accuracy of the kernel GRNN estimate is critically influenced by the choice of the bandwidth. Given the need of data-driven bandwidth specification we propose several automatic selection methods that are compared by means of simulation studies. Our experiments reveal that the method referred to as the partitioned cross-validation algorithm can be recommended as the practical procedure for the bandwidth choice for the kernel GRNN estimate in terms of its statistical accuracy and implementation aspects.

Implementation of Efficient Reconfigurable FIR Filter With Control Logic for 5G Applications

Filters are used to achieve frequency selectivity on the spectrum of input signal. Due to the stability of FIR filters, they are used in most of the applications. In the conventional FIR filters the frequency band is fixed and can‟t be changed once it is designed. Hence there is a necessity of an FIR filter with auto adjustment of band width. The design of FIR filter requires more number of filter coefficients to get the desired bandwidth specification. This results in a large slice for FPGA implementation. Here it is proposed a state machine to select different FIR filters with the designated set of coefficients. Each FIR filter is having different set of coefficients and based on the frequency of the clock signal the FIR filter is selected. Therefore frequency selectivity can be achieved. The Proposed method is to implement Reconfigurable FIR Filter with control logic for auto adjustment of fre-quency selections to achieve better band width requirements. The filter order is initially selected as 4 and presented the simulation results. The order of the filter(n) increased to 24 for verifying the bandwidth selection. The proposed architecture is compared with the existing architecture with 16bits and 11taps. Simulation results presented are verified using Xilinx ISE design suite 14.7. Total number of 4 input LUTs utilized are 630 for n=24. Power consumed by the overall design is 195mW.

Penerapan Model Geographically Dan Temporally Weighted Regression Pada Kecelakaan Lalu Lintas

Geographically and temporally weighted regression (GTWR) is a model that is used to deal with instability in data both spatially and temporally and to produce local parameters. In this paper, The GTWR model is used to analyze the factors that are thought to significantly influence the number of traffic accidents in Mempawah Regency. The data used in this study came from 8 districts with the variables used were the number of traffic accidents, the number of population (gender ratio, length of damaged road conditions, and percentage of adolescence. The parameter estimation of the GTWR model was obtained using the weighted least square (WLS) method. The optimal bandwidth selection uses the Cross-Validation (CV) method and the weighting used is the Fixed bisquare function. The results of the analysis show that using the GTWR model, it was found that only the population size variable significantly affected the number of traffic accidents in all locations in Mempawah Regency from 2015 to 2018. The GTWR model was known to be better than the multiple regression model because it produced smaller AIC and RSS values and a larger R-square value.