Search

Uncertain time series analysis is an effective method to predict the variable with time index under imprecise observations. Sometimes, the time series model is built directly on the data which the prediction result is inaccurate. In this paper, an uncertain vector autoregressive smoothly moving average model (UVARSMA) is given. The least absolute deviation estimation and the least square estimation are given to estimate the unknown parameters. In order to predict effectively, we analyze the residuals and give the point estimation and interval estimation about the prediction. The relevant results are compared with those of the uncertain vector autoregressive model. Finally, a practical example about air index in Beijing, from 9 March 2022 to 23 April 2022, is given to verify the feasibility and accuracy of the new model. [ABSTRACT FROM AUTHOR]

In this paper, an estimation of the multicomponent stress-strength reliability is introduced subject to the exponentiated generalized inverse Rayleigh distribution. Different methods of estimation are introduced to estimate the multicomponent stress-strength reliability. Simulation method is introduced to illustrate the steps of finding the estimates of the multicomponent stress-strength reliability. Asymptotic and bootstrap confidence intervals are proposed in order to find interval estimations for the multicomponent stress-strength reliability. A Bayesian estimation method is introduced for the multicomponent stress-strength reliability. A simulation study is introduced to obtain the estimates of the multicomponent stress-strength reliability for the different methods of estimation. A real data application is introduced to show how the exponentiated generalized inverse Rayleigh distribution is used to fit the real data sets. [ABSTRACT FROM AUTHOR]

AbstractOrdinary least square estimators for linear regression models are highly influenced by outliers. Copula is a powerful tool for studying the dependence between response and predictor variables and may overcome several limitations associated with the classical linear regression models. In this study, we investigate the efficacy of the copula-based regression models over classical linear regression in the presence of outliers in the x, y, and x-y directions. We also examine the performance of several robust regression estimation techniques when variables are related via elliptical and Archimedean family of copulas and outliers are present in the data. Finally, real and artificial data sets are analyzed to compare the performance of various robust regression techniques. [ABSTRACT FROM AUTHOR]

Battlefield surveillance radar is usually 2-D radar, which cannot realize target three-dimensional localization, leading to poor resolution for the air target in the elevation dimension. Previous researchers have used the Traditional Height Finder Radar (HFR) or multiple 2-D radar networking to estimate the target three-dimensional location. However, all of them face the problems of high cost, poor real-time performance and high requirement of space–time registration. In this paper, Reconfigurable Intelligent Surfaces (RISs) with low cost are introduced into the 2-D radar to realize the target three-dimensional localization. Taking advantage of the wide beam of 2-D radar in the elevation dimension, several Unmanned Aerial Vehicles (UAVs) carrying RISs are set in the receiving beam to form multiple auxiliary measurement channels. In addition, the traditional 2-D radar measurements combined with the auxiliary channel measurements are used to realize the target three-dimensional localization by solving a nonlinear least square problem with a convex optimization method. For the proposed RIS-assisted target three-dimensional localization problem, the Cramer–Rao Lower Bound (CRLB) is derived to measure the target localization accuracy. Simulation results verify the effectiveness of the proposed 3-D localization method, and the influences of the number, the positions and the site errors of the RISs on the localization accuracy are covered. [ABSTRACT FROM AUTHOR]

In this article, a novel population selection method, fitness distance balance (FDB), and predictive candidate (PC) solution generation hybridization with starling murmuration optimizer (SMO), FDBPC-SMO are proposed. In FDBPC-SMO algorithm, FDB selects subpopulations instead of the separating search strategy (SSS) in the original SMO. The separating size determined in SMO is given as input to the FDB, and the FDB generates the subpopulation based on the distances among the populations. The least squares strategy is applied to the population obtained at the end of the SMO, and the estimated population candidates are found and replaced with the worst solution candidates from the original population. By adding qualitative analysis, the effectiveness of the FDBPC-SMO has been examined based on the dimension and iteration. The success of FDBPC-SMO is the selection of more efficient candidate solutions from the previous population at each iteration, thus minimizing the possibility of getting stuck in the local optimum. The performance of FDBPC-SMO has been investigated on CEC2017 and CEC2019 test sets and seven engineering application problems. In addition, Wilcoxon and Friedman statistical tests confirm the convergence and fitness results of the proposed method. Accordingly, comparing to conventional and improved methods, it is clear that the convergence ability of FDBPC-SMO is superior. [ABSTRACT FROM AUTHOR]

Summary: The state‐of‐charge (SOC) of the battery is the most critical parameter for an electric vehicle (EV) dashboard. Generally, the Coulomb counting (CC) method is used for SOC estimation, but the conventional CC method has some errors due to initial SOC determination. This work uses battery modeling and parameter estimation of a lithium‐iron‐phosphate (LFP) battery to determine the initial SOC. The equivalent circuit models (ECMs) have been used for battery parameters estimation by the least‐squares estimation (LSE) algorithm. This algorithm uses only experimental terminal voltage and current data from Arbin battery testing system. The ECM parameters vary with battery SOC, and the differential capacity plot has explained the variation. SOC can be estimated using estimated parameters and the relationship between SOC and open‐circuit voltage (OCV). Additionally, a modified CC method is proposed to solve the problem of estimating initial SOC by determining an OCV‐SOC relation using experimental data. The modeling of the battery is validated by comparing ECMs with physical battery terminal voltage profiles. [ABSTRACT FROM AUTHOR]

Battlefield surveillance radar is usually 2-D radar, which cannot realize target three-dimensional localization, leading to poor resolution for the air target in the elevation dimension. Previous researchers have used the Traditional Height Finder Radar (HFR) or multiple 2-D radar networking to estimate the target three-dimensional location. However, all of them face the problems of high cost, poor real-time performance and high requirement of space–time registration. In this paper, Reconfigurable Intelligent Surfaces (RISs) with low cost are introduced into the 2-D radar to realize the target three-dimensional localization. Taking advantage of the wide beam of 2-D radar in the elevation dimension, several Unmanned Aerial Vehicles (UAVs) carrying RISs are set in the receiving beam to form multiple auxiliary measurement channels. In addition, the traditional 2-D radar measurements combined with the auxiliary channel measurements are used to realize the target three-dimensional localization by solving a nonlinear least square problem with a convex optimization method. For the proposed RIS-assisted target three-dimensional localization problem, the Cramer–Rao Lower Bound (CRLB) is derived to measure the target localization accuracy. Simulation results verify the effectiveness of the proposed 3-D localization method, and the influences of the number, the positions and the site errors of the RISs on the localization accuracy are covered.

Accelerated Degradation Testing (ADT) provides an efficient experimental approach to collect lifetime-related data for the reliability assessment of highly reliable products under normal use stress. Recoverability, which occurs in many typical failure modes when online in-chamber measurements have to be replaced by offline ex-chamber measurements with stress released, is an important factor that may affect the accuracy of reliability estimation from ADT data. Nonetheless, the presence of recoverability has not been adequately considered in traditional methods, leading to an over-optimistic estimation of lifetime and reliability. The linkage between recoverability and such inferential bias has not been theoretically studied systematically. In this study, recoverability is explicitly incorporated into the ADT modeling framework. Without loss of generality, the Wiener process is adopted as the basis for the proposed degradation model, superimposed with a cumulative recovery. Theoretical results show that the Mean Time To Failure (MTTF) will be overestimated with recoverability neglected, which leads to poor lifetime-centered decision-making. For finite and even small sample sizes, this conclusion is not intuitively certain but the chance is still high and the corresponding overestimation probability can be calculated explicitly. All theoretical conclusions are validated by simulation studies, in which the MTTFs are overestimated from 6% to 42% under different parameter settings. The real-world application of semiconductor products shows that even slight recoverability could lead to an obvious overestimation of MTTF and the proposed model provides a convenient way to derive more accurate assessment results. [ABSTRACT FROM AUTHOR]

Abstract Uncertain time series analysis is an effective method to predict the variable with time index under imprecise observations. Sometimes, the time series model is built directly on the data which the prediction result is inaccurate. In this paper, an uncertain vector autoregressive smoothly moving average model (UVARSMA) is given. The least absolute deviation estimation and the least square estimation are given to estimate the unknown parameters. In order to predict effectively, we analyze the residuals and give the point estimation and interval estimation about the prediction. The relevant results are compared with those of the uncertain vector autoregressive model. Finally, a practical example about air index in Beijing, from 9 March 2022 to 23 April 2022, is given to verify the feasibility and accuracy of the new model. [ABSTRACT FROM AUTHOR]

In this study, we propose a new three-parameter lifetime model based on the type-I half-logistic G family and the unit-Gompertz model, which we named the half-logistic unit Gompertz type-I distribution. The key feature of such a novel model is that it adds a new tuning parameter to the unit-Gompertz model using the type-I half-logistic family in order to make the unit-Gompertz model more flexible. Diagrams and numerical results are used to look at the new model's mathematical and statistical aspects. The efficiency of estimating the distribution parameters is measured using a variety of well-known classical methodologies, including Anderson–Darling, maximum likelihood, least squares, weighted least squares, right tail Anderson–Darling, and Cramer–von Mises estimation. Finally, using the maximum likelihood estimation method, the flexibility and ability of the proposed model are illustrated by means of re-analyzing two real datasets, and comparisons are provided with the fit accomplished by the unit-Gompertz, Kumaraswamy, unit-Weibull, and Kumaraswamy beta distributions for illustrative purposes. [ABSTRACT FROM AUTHOR]

The mathematical characteristics of the mixture of Lindley model with 2-component (2-CMLM) are discussed. In this paper, we investigate both the practical and theoretical aspects of the 2-CMLM. We investigate several statistical features of the mixed model like probability generating function, cumulants, characteristic function, factorial moment generating function, mean time to failure, Mills Ratio, mean residual life. The density, hazard rate functions, mean, coefficient of variation, skewness, and kurtosis are all shown graphically. Furthermore, we use appropriate approaches such as maximum likelihood, least square and weighted least square methods to estimate the pertinent parameters of the mixture model. We use a simulation study to assess the performance of suggested methods. Eventually, modelling COVID-19 patient data demonstrates the effectiveness and utility of the 2-CMLM. The proposed model outperformed the two component mixture of exponential model as well as two component mixture of Weibull model in practical applications, indicating that it is a good candidate distribution for modelling COVID-19 and other related data sets.

The establishment of failure risk assessment on the secondary system in smart substation can help power gird company timely arrange maintenance when the failure risk of equipment accumulates to a certain extent,thus effectively preventing systematic failure. In this paper,a probabilistic failure model is established through Weibull probability distribution function from a perspective of reliability engineering. According to the type tests and various daily operation statistics of secondary equipment,the regression equation calculated by the average rank method and least square estimation can obtain the model parameter estimation. By doing so,the equipment failure model close to the real operation condition can be obtained. Moreover,the relationship between the failures of the secondary system and a single equipment is established through the fault tree. Therefore,the risk probability distribution function of top event in secondary system can be calculated quantitatively,which can provide auxiliary decision for risk warning and state overhaul. Finally,it is verified by the numerical results that the proposed secondary equipment failure model can provide a reference for accurate time arrangement for system maintenance.

In this paper, we consider the parameter estimation problem for discretely observed Dothan model driven by small symmetrical noise. The least square method is applied to obtain the drift parameter estimators. The consistency and asymptotic distribution of the estimators are derived when a small dispersion coefficient ε → 0. Some simulations are made to demonstrate the applicability of the results. [ABSTRACT FROM AUTHOR]

In this paper, we propose a two-component mixture of Akash model (TC-MAM). The behavior of TC-MAM distribution has been presented graphically. Moment-based measures, including skewness, index of dispersion, kurtosis, and coefficient of variation, have been determined and hazard rate functions are presented graphically. The probability generating function, Mills ratio, characteristic function, cumulants, mean time to failure, and factorial moment generating function are all statistical aspects of the mixed model that we explore. Furthermore, we figure out the relevant parameters of the mixture model using the most suitable methods, such as least square, weighted least square, and maximum likelihood mechanisms. Findings of simulation experiments to examine behavior of these estimates are graphically presented. Finally, a set of data taken from the real world is examined in order to demonstrate the new model's practical perspectives. All of the metrics evaluated favor the new model and the superiority of proposed distribution over mixture of Lindley, Shanker, and exponential distributions. [ABSTRACT FROM AUTHOR]

This paper develops a simple and computationally efficient parametric approach to the estimation of general hidden Markov models (HMMs). For non-Gaussian HMMs, the computation of the maximum likelihood estimator (MLE) involves a high-dimensional integral that has no analytical solution and can be difficult to approach accurately. We develop a new alternative method based on the theory of estimating functions and a deconvolution strategy. Our procedure requires the same assumptions as the MLE and deconvolution estimators. We provide theoretical guarantees about the performance of the resulting estimator; its consistency and asymptotic normality are established. This leads to the construction of confidence intervals. Monte Carlo experiments are investigated and compared with the MLE. Finally, we illustrate our approach using real data for ex-ante interest rate forecasts. [ABSTRACT FROM AUTHOR]

This paper mainly presents the parameter identification method developed from a Least Square Estimation (LSE) algorithm to estimate hydrodynamic coefficients of Autonomous Underwater Vehicle (AUV) in the presence of measurement biases. LSE based parameter determination method is developed to obtain unbiased estimated values of hydrodynamic coefficients of AUV from biased Inertial Navigation System (INS) measurements. The proposed parameter identification method consists of two phases: in the first phase, high precision INS and its auxiliary instrument including compass, pressure depth sensor, and Doppler Velocity Log (DVL) are designed as Integrated Navigational System coupled with Complementary Kalman Filter (CKF) to determine hydrodynamic coefficients of AUV by removing the INS measurement biases; in the second phase, LSE based parameter identification method is applied to the model in the first phase for obtaining unbiased estimated values of hydrodynamic coefficients of AUV. In this paper, a method for identifying the yaw and sway motion dynamic parameters of an AUV is given. Various maneuvering scenarios are verified to assess the parameter identification method employed. The simulation results indicate that using the CKF based Integrated Navigation System together with unbiased measurement conversion could produce better results for estimating the hydrodynamic coefficients of AUV. [ABSTRACT FROM AUTHOR]

The coronavirus disease outbreak in 2019 (COVID-19) has caused major economic and healthcare problems worldwide. At this time, the worldwide outbreak has passed its peak, while the greatest number of cases has been in the USA, Brazil, and India. Measures and policies for controlling the outbreak have been developed by authorities to protect the population of each country, and forecasting the number of infectious people is an important factor for developing them. This research was conducted to identify a suitable forecasting model for estimating the cumulative daily number of infectious people worldwide. Sample countries with severe outbreaks were selected from each continent. Herein, forecasting models based on logistic, Richards, and Gompertz growth curves are derived and their suitability for forecasting the COVID-19 rates in each sample country and worldwide are analyzed. Moreover, estimating the growth curve parameters is based on the least-squares method. The results show that the Gompert growth curve is the most suitable for estimating the cumulative number of infectious people worldwide.

Copyright of Telecommunication Engineering is the property of Telecommunication Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In recent years, statisticians have grown increasingly engaged in research of mixture models, particularly in the previous decade, without adequate consideration of challenge of estimating the parameters of mixture models from a frequentist perspective. Except for maximum likelihood estimation, this study addresses this vacuum by discussing the two other classical methods of estimation for mixture model. We commence by briefly describing the three frequentist approaches, namely maximum likelihood, ordinary, and weighted least squares, and then comparing them through extensive numerical simulations. The model’s applicability is illustrated by its application to simulated and real-world data, which yields promising results in terms of enhanced estimation.

We propose a new model with flexible failure rate called complementary Poisson generalized half logistic (CPGHL). Various properties of the model are explored and examine numerically such as the explicit expressions of the moments, mean deviations, Bonferroni and Lorenz curves, Shannon and Renyi entropy. The distribution of mixture of two CPGHL and some related models based on the log-transform of CPGHL are discussed. The asymptotic of moments of residual life and asymptotic distribution of order statistics are obtained. The characterization of Poisson half logistic (PHL) by truncated moments of a certain function of a random variable is discussed. Estimation of the model parameters was approached by maximum likelihood, least square, and percentile methods. Further, the estimation by maximum likelihood for right censored data of the model were considered. The proposed estimation techniques were assessed by simulation studies. Three data applications are provided one of them is a censored data to demonstrate how the new model outperforms some other existing distribution in practice.

This article presents a novel discrete distribution with a single parameter, called the discrete Teissier distribution. It is noted that this model, with one parameter, offers a high degree of fitting flexibility as it is capable of modelling equi-, over-, and under-dispersed, positive and negative skewed, and increasing failure rate datasets. In this article, we have explored its numerous essential distributional features such as recurrence relation, moments, generating function, index of dispersion, coefficient of variation, entropy, survival and hazard rate functions, mean residual life and mean past life functions, stress-strength reliability, order statistics, and infinite divisibility. The classical point estimators have been developed using the method of maximum likelihood, method of moment, and least-squares estimation, whilst an interval estimation based on Fisher's information has also been presented. Finally, the applicability of the suggested discrete model has been demonstrated using two complete real datasets. [ABSTRACT FROM AUTHOR]

In this study, we propose a new three-parameter lifetime model based on the type-I half-logistic G family and the unit-Gompertz model, which we named the half-logistic unit Gompertz type-I distribution. The key feature of such a novel model is that it adds a new tuning parameter to the unit-Gompertz model using the type-I half-logistic family in order to make the unit-Gompertz model more flexible. Diagrams and numerical results are used to look at the new model’s mathematical and statistical aspects. The efficiency of estimating the distribution parameters is measured using a variety of well-known classical methodologies, including Anderson–Darling, maximum likelihood, least squares, weighted least squares, right tail Anderson–Darling, and Cramer–von Mises estimation. Finally, using the maximum likelihood estimation method, the flexibility and ability of the proposed model are illustrated by means of re-analyzing two real datasets, and comparisons are provided with the fit accomplished by the unit-Gompertz, Kumaraswamy, unit-Weibull, and Kumaraswamy beta distributions for illustrative purposes.

In this short note, we give the representation of the non symmetric Rosenblatt process as a Wiener–Itô multiple integral with respect to the Brownian motion on a finite interval. Based on this representation, we obtain a least square-type estimator for an unknown parameter of the drift coefficient of a simple model driven by the non symmetric Rosenblatt process. [ABSTRACT FROM AUTHOR]

This work investigates single-input single-output (SISO) visible light communication (VLC) when subject to signal-dependent shot noise (SDSN). The topics of discussion include channel estimation and data transmission, where in the former, we introduce both least square (LS) and maximum likelihood (ML) estimators. The Cramér-Rao lower bound (CRLB) of the channel estimation error is also derived. In terms of data transmission, we propose optimal and sub-optimal receiver designs and present their bit error rate (BER) performances. In specific, we derive a closed-form expression of the BER for the sub-optimal receiver and an approximated version for the optimal one. Our analysis indicates that the performance of the CRLB demonstrates no linear relationship with the SDSN, thermal noise, or the fading channel. On the other hand, SDSN has quite a severe effect on the channel estimation error bound, and as such, it can dramatically degrade the BER performance. Heightened performance degradation can also be explained by the joint effects of the channel estimation error and SDSN. [ABSTRACT FROM AUTHOR]

Purpose: While sustainable development policies are mostly set based on United Nations (UN) geoscheme classification, no study attempts to examine the impact of influential economic variables such as energy consumption (EC) and merchandise exports (ME) on carbon dioxide (CO2) emission in the UN geoscheme regions. The purpose of this paper is to examine the possible impact of EC and ME on CO2 emission in UN geoscheme classification regions such as Africa, America, Arab, Asia and Europe. Design/methodology/approach: This paper uses autoregressive distributed lag (ARDL), Pedroni panel cointegration and panel Granger causality methodologies covering an annual panel data sampling period from 1971 to 2014. Findings: The results show that there is bidirectional causality between all three variables in the European and American panel except for the non-causality from CO2 to EC in the American panel. These findings suggest possible consequences of weaker energy efficiency (even under environmental policy tightening) and strong demand for energy-intensive economic activities in those regions. Developed countries with higher environmental policy tightening (America and Europe) show significant estimates from the chosen tests supporting the Porter hypothesis. EC and ME have a long-run impact on CO2 emission in American and European panels. The African region has the least environmental impact of pollution from ME. Practical implications: The ME and EC have a direct significant impact on CO2 emission in America and Europe. As these causalities, co-integrations and their impacts share a long-run equilibrium relationship, policymakers must design long-term industry policies such as cleaner production techniques focusing on environmentally sustainable practices. Also, it is suggested that the policymakers must ensure that they implement more robust policies and standards for environmental-friendly export production. Originality/value: This is the first paper that examines the impact of EC and ME on CO2 emission in UN geoscheme regions. The findings of this paper provide theoretical implications supporting Porter hypothesis and practical implications for policymaking. [ABSTRACT FROM AUTHOR]

In this work, we study the problem of fusing one Pedestrian-Dead-Reckoning-based (PDR-based) position measurement and one instant Received-Signal-Strength-based (RSS-based) position measurement. This situation can arise in a smartphone-based indoor positioning system when we want to locate a moving user in real-time with sustainable accuracy, but the RSS sampling ability of smartphones is limited; for example, one RSS sample per second. Firstly, by investigating RSS’s heterogeneity, we offer a solution for RSS-based continuous positioning problems under a low RSS sampling rate that satisfies real-time requirements. Secondly, we propose a method to improve accuracy for the RSS-based position estimation method, i.e., multilateration using Least Square Estimation. We consider PDR-based and improved RSS-based positions both have Gaussian uncertainty due to initial position plus drifting and RSS-to-distance conversion, respectively. Then, the Kalman filter will fuse two kinds of Gaussian distribution to produce more precise positions. The method is intended to design a real-time system for locating a moving target. Experiments are conducted in real indoor space with a commodity device. Its results show that our proposed solution is highly accurate and feasible in actual deployment. [ABSTRACT FROM AUTHOR]

To extract valid results from time series analysis of tides observations, noise reduction is vital. This study aimed to use a precise statistical model to investigate noise types. Noise component amplitude of the proposed model was studied by Least Square Estimation (LS-VCE) through different statistical models: (1) white noise and auto-regressive noise, (2) white noise and Flicker noise, (3) white noise and random walk noise, (4) white noise and Flicker noise and random walk, and (5) auto-regressive noise and Flicker noise. Based on the values obtained for the Likelihood Function, it was concluded that the noise model that can be considered for observations of the Buoy time series includes two white and Flicker noises. In addition, tide forecasting for all stations was done by extracting important frequency calculated in two cases: (1) the first case in which matrix of observation weight matrix was considered as the unit matrix or the noise model was just a white noise (2) the case in which matrix of observation weight matrix was considered as a combination of white and Flicker noises. The results show that use of precise observation weight matrix resulted in 11 millimeter difference compared to the case in which observation with unit weight matrix was used.

In this paper we have adapted a delayed dengue model to Zika. By assuming that the epidemic starts by a single infected individual entering a disease-free population at some initial time t0 we have used the least squares parameter estimation technique in R to estimate the initial time t0 using observed Zika data from Brazil as well as the transmission probabilities of Zika in Brazil between humans and mosquitoes and vice-versa. Different values of Aedes aegypti (A. aegypti) biting rate are used throughout the paper. We have estimated the value of the basic reproduction number for Zika in Brazil and calculated the expected number of cases of microcephaly in newborns as a result of women infected with Zika during pregnancy. We started off with a non-age-structured model then introduced age-structure into the model. However in reality seasonality, in particular temperature and rainfall, have a great impact on the population size of A. aegypti. Hence we repeat both the non-age-structured and age-structured analyses introducing seasonality into the A. aegypti birth function to model the effect of these environmental factors.

In this paper, we propose a two-component mixture of Akash model (TC-MAM). The behavior of TC-MAM distribution has been presented graphically. Moment-based measures, including skewness, index of dispersion, kurtosis, and coefficient of variation, have been determined and hazard rate functions are presented graphically. The probability generating function, Mills ratio, characteristic function, cumulants, mean time to failure, and factorial moment generating function are all statistical aspects of the mixed model that we explore. Furthermore, we figure out the relevant parameters of the mixture model using the most suitable methods, such as least square, weighted least square, and maximum likelihood mechanisms. Findings of simulation experiments to examine behavior of these estimates are graphically presented. Finally, a set of data taken from the real world is examined in order to demonstrate the new model’s practical perspectives. All of the metrics evaluated favor the new model and the superiority of proposed distribution over mixture of Lindley, Shanker, and exponential distributions.