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Ridge Estimation (RE) is a widespread method to overcome the problem of collinearity defining a class of estimators depending on the non-negative scalar parameter k. A great number of papers focus on the estimation of this biasing parameter. Traditionally, the mean squared error criterion is used to compare the performance of the different proposed estimators. However, the minimization of the mean squared error (MSE) does not always guarantee the mitigation of collinearity, meaning it is possible, for example, to obtain a variance inflation factor (VIF) higher than 10 for the k that minimizes the MSE. In this paper, we propose the VIF criteria to select the biased ridge parameter. A Monte Carlo simulation is presented with results that support this idea. Also, two real life empirical applications are used to illustrate the contribution of this paper. [ABSTRACT FROM AUTHOR]

In this paper we introduce a novel statistical framework based on the first two quantile conditional moments that facilitates effective goodness-of-fit testing for one-sided Lévy distributions. The scale-ratio framework introduced in this paper extends our previous results in which we have shown how to extract unique distribution features using conditional variance ratio for the generic class of α-stable distributions. We show that the conditional moment-based goodness-of-fit statistics are a good alternative to other methods introduced in the literature tailored to the one-sided Lévy distributions. The usefulness of our approach is verified using an empirical test power study. For completeness, we also derive the asymptotic distributions of the test statistics and show how to apply our framework to real data. [ABSTRACT FROM AUTHOR]

The probability of ruin of an insurance company is one of the main risk measures considered in risk theory, and the problems of its calculation and approximation have attracted much attention. Statistical estimations have been developed on the ruin probability in infinite time for insurance loses from heavy-tailed distributions. However, these estimation suffer heavily from under-coverage or have a robustness problem. We therefore need another method for estimating the probability of ruin in infinite time for heavy-tailed losses. In this paper, we introduce a robust estimator of the infinite-time probability of ruin for such distributions. Our methodology is based on extreme value theory, which offers adequate statistical results for such distributions. Our approach is based on a sensitive distribution known as the t-Hill estimator (t-score or score moment estimation) for the index of any tail distribution and introduced in Fabiáan and Stehlík. We establish their asymptotic normality, and through a simulation study, illustrate their behaviour in terms of absolute bias and mean squared error. The simulation results show that our estimators perform well and that they are fairly robust to outliers. [ABSTRACT FROM AUTHOR]

Metric regularity is widely concerned since its important applications in optimization and control theory. For promoting the application of metric regularity, it is valuable to study the estimation of the neighborhood which makes the regularity hold. However, it seems that no result has been established about this issue. This paper investigates the estimation of the neighborhood of metric regularity for quadratic functions. The main result gives the expression of the neighborhood of metric regularity for a kind of convex quadratic functions. [ABSTRACT FROM AUTHOR]

In this paper we introduce first-order spatial random coefficient non-negative integer-valued autoregressive (SRCINAR(1,1)) model to fit the spatial count data on a two-dimensional regular lattice. We use a thinning operator in order to define the processes. The mean, variance and autocorrelation function of the model are derived. Three methods (Yule-Walker, Conditional least squares and Conditional maximum likelihood) for the estimation of the parameters of the model are discussed and are compared with respect to the root mean squared error (RMSE), in a simulation study. Finally, SRCINAR(1,1) model is applied to a real data set of striga counts at the ICRISAT Sahelian Center near Niamey (Niger). [ABSTRACT FROM AUTHOR]

In this paper, we consider the on-line estimation of current subsequences for Partially Observable P-time Petri Nets and their starting markings on a sliding horizon composed of steps defined by two successive occurrences of observable transition firings. We propose a general strategy composed of two phases: Phase 1 exploits a simplification of the P-time Petri net under the form of a Timed Petri net; considering a candidate count vector and the relevant starting marking proposed at Phase 1, Phase 2 makes a schedulability analysis by building a system of relations which can be represented by an acyclic conflict-free computation graph. The complete approach avoids the generation of sets which is generally time and space consuming, and provides an optimal solution for each subproblem by using efficient standard tools. [ABSTRACT FROM AUTHOR]

This paper investigates how ancillary geographic data – in particular, information on land or assessor parcels – might be used to improve estimates for small area populations and population characteristics. It seeks to determine whether parcel land use codes can be used to reliably replicate population and housing distributions within small (subcounty) areas and whether other parcel attributes – in addition to land use – exhibit any explanatory power in replicating population and housing characteristics within these same places. The basis for this paper was Professor Barbara Buttenfield’s service on the Census Scientific Advisory Committee, in which her working group explored the utility of administrative source data as an alternative or complement to federal survey data. This analysis highlights some of the benefits and complications of the incorporation of parcel data into geodemographic estimation, and the findings demonstrate that such a use is problematic but encouraging. [ABSTRACT FROM AUTHOR]

AbstractThis paper wishes to propose a new model for count data and it is shortly called a Zero-Inflated Poisson - Probit (ZIP-P) model. The way of setting up for this new model is also based on the traditional model, the Zero-Inflated Poisson (ZIP) model. Note that this new model as well as the ZIP model has a wide range of practical applications and it can be easily applied and analyzed to the real data sets. More specifically, both of these models can be applied very well to count data sets where the frequency of zeros occupies a very high percentage. To achieve the goal of this study, we first recall the construction and general formula of the ZIP model. Then we present the most detailed way for building a new model includes the general formula and related equations necessary for estimating the parameters of the ZIP-P model. In addition, asymptotic results, numerical simulations and analysis of a real data set are also performed in this work. Moreover, we also clarify that this actual data set fits the ZIP-P model better than the other models considered. Finally, several lines of discussions and inferences are made in the study. [ABSTRACT FROM AUTHOR]

In this paper, we propose a new bivariate random coefficient autoregressive (BOD-RCAR(1)) process driven by both explanatory variables and past observations. Firstly, some statistical properties of this model are derived. Secondly, three methods are used for estimating the unknown parameters: conditional least squares (CLS), conditional maximum likelihood (CML) and maximum empirical likelihood (MEL). The asymptotic properties of the estimators are given. Besides, two kinds of test based on empirical likelihood (EL) are established. A simulation experiment is presented to demonstrate the performance of the proposed method. Finally, an application to a real data example is investigated to assess the performance of the model. [ABSTRACT FROM AUTHOR]

The paper presents an improvement of the n-shifting technique to identify the frequency response of an industrial process using a fully asymmetric and delaying relay. The n-shifting approach allows the calculation of n + 1 points of G(s) by an asymmetric relay experiment. This set of n points is composed of G(0), G(jωosc), ... , G(jnωosc), being ωosc the oscillation frequency, and where G(jωosc) is in most cases located in the third quadrant of the Nyquist map. By delaying the relay output and repeating a similar experiment it can be generated n additional points of G(s) where the first point is G(jω'osc) with 0 < ω'osc < ωosc. In this way, it is possible to depict the full output spectrum of G(s) from zero to very high frequencies by a short relay experiment. An example of identification and tuning of a PID controller with data from the n-shifting are presented to show the validity of the approach. [ABSTRACT FROM AUTHOR]

Abstract This study proposes a new model in the family zero-inflated (ZI) binary models, it is called as a ZI Probit Bernoulli (ZIPBer) model. This model can be performed to model or simulate binary data that has an excessive amount of zero counts. At the framework of this research, we first introduce the classical formula, estimating equation (EE) and related functions for the ZIPBer model. Next, the results of asymptotic inferences for the ZIPBer model is studied based on the widespread regularity conditions. For the numerical illustrations, many simulation studies and a practical data set are executed in this paper. Specifically, we perform the fishing data set (FDS) to check the working effectiveness and robustness of the maximum likelihood estimation (MLE) approach in estimating parameters for the ZIPBer model. We also illustrate that this actual data set is more suitable for the proposed model: ZIPBer than other binary models such as logit, probit and ZIBer. The achieved results in this study are very unanimous with reality. It also illustrates that this is a dependable verification for us to know the solution as well as how to proceed to how to catch or take the most fish. This is the contribution of this work in terms of application. Finally, some crucial discussions and conclusions are given in this paper. [ABSTRACT FROM AUTHOR]

AbstractPeriodic autoregressive (PAR) time series with finite variance is considered as one of the most common models of second-order cyclostationary processes. However, in the real applications, the signals with periodic characteristics may be disturbed by additional noise related to measurement device disturbances or to other external sources. Thus, the known estimation techniques dedicated for PAR models may be inefficient for such cases. When the variance of the additive noise is relatively small, it can be ignored and the classical estimation techniques can be applied. However, for extreme cases, the additive noise can have a significant influence on the estimation results. In this paper, we propose four estimation techniques for the noise-corrupted PAR models with finite-variance distributions. The methodology is based on Yule-Walker equations utilizing the autocovariance function. It can be used for any type of the finite-variance additive noise. The presented simulation study clearly indicates the efficiency of the proposed techniques, also for extreme case, when the additive noise is a sum of the Gaussian additive noise and additive outliers. The proposed estimation techniques are also applied for testing if the data correspond to noise-corrupted PAR model. This issue is strongly related to the identification of informative component in the data in case when the model is disturbed by additive non-informative noise. The power of the test is studied for simulated data. Finally, the testing procedure is applied for two real time series describing particulate matter concentration in the air. [ABSTRACT FROM AUTHOR]

Abstract Process capability analysis is a widely used process improvement metric. There are several capability indices used in industries to assess the performance of production process. C p m is one of the most important capability index which is often used in many industries for assessing the performance of the production process by taking into consideration the target value. Although capability indices are estimated under the assumption that the data are independent but in many industries, process outputs are often autocorrelated. At the same time, no measuring device gives accurate results. In this paper, we discuss the statistical properties of the estimator of C p m for autocorrelated data and in the presence of random measurement errors. Here, we show that under the combined effect of autocorrelation and measurement errors, the estimator of C p m behaves differently depending upon the variability of the measurement errors. [ABSTRACT FROM AUTHOR]

The Pareto Distribution has been found to be useful in describing socio-economic, physical, and biological phenomena. This paper explores different methods of estimating the shape parameter of the Pareto distribution and two of its functions (reliability and hazard functions), assuming the scale parameter is known. In particular, this paper shows that the Bayesian approach, using both the quadratic and entropy loss functions, can provide a general method for estimating the shape parameter and these two functions. These Bayesian estimators generalize the maximum likelihood estimator (MLE) and the uniformly most powerful unbiased estimator (UMVUE). Simulation results show that the general Bayesian method can perform better than the MLE and UMVUE, especially when the sample size is small. [ABSTRACT FROM AUTHOR]

In this paper, two-parameter Poisson binomial-exponential 2 (PBE2) distribution is firstly reviewed, then a new integer-valued autoregressive (INAR) model with PBE2 innovations is proposed. The definition and statistical properties of the proposed model are given, including the mean, variance, covariance, strict stationarity and ergodicity. Two-step conditional least squares and conditional maximum likelihood estimation methods are considered to estimate the parameters. To assess the proposed model, a crime data set is analyzed and a comparison with other competing INAR models is given, which shows that the proposed model yields a better performance. [ABSTRACT FROM AUTHOR]

This paper proposes a new statistical method for one-way classification which is based on the (reversed) hazard function of the response variable. The model parameters are estimated according the maximum likelihood approach. Several testing procedures, e.g., generalized likelihood ratio test, are investigated to assess homogeneity of populations. A non-parametric method for data analysis is also proposed. Two data sets are studied using the obtained results. [ABSTRACT FROM AUTHOR]

In this paper, a statistical model for COVID-19 infection dynamics is described, using only the observed daily statistics of infected individuals. For this purpose, two special classes of branching processes without or with an immigration component are considered. These models are intended to estimate the main parameter of the infection and to give a prediction of the mean value of the non-observed population of the infected individuals. This is a serious advantage in comparison with other more complicated models where the officially reported data are not sufficient for estimation of the model parameters. The model is applied for different regions in the world and the corresponding parameters of the infection dynamics are estimated. [ABSTRACT FROM AUTHOR]

In this paper, we propose a tractable Kumaraswamy Bell exponential (KwBE) distribution as a submodel of the Kumaraswamy Bell-G family of distributions. Several well-established properties are obtained for the KwBE distribution, such as the linear functional representation, $r$ r th moment, incomplete moment, moment generating function using Wright generalized hyper-geometric function, conditional moment and Réyni entropy. Based on the KwBE model, a group acceptance sampling plan (GASP) for the truncated life test is presented using median life as a quality index. Moreover, the essential design parameters are derived by defining the consumer risk and the test termination duration. The comparative study of GASP with ordinary sampling plan (OSP) is performed. A simulation study is performed to highlight the behavior of the estimates. On the inferential side, the associated parameters are estimated using a well-established maximum likelihood estimation method. The detailed model's comparison analysis, graphical as well as numerical evidence to real-data applications, supports the theoretical work. [ABSTRACT FROM AUTHOR]

Estimation of bicycling volumes is essential for the strategic implementation of infrastructure and related transport elements and policies. Link-level volume estimation models (models that estimate volumes on individual street segments) allow for understanding variation in bicycling volumes across an entire network at higher spatial resolution than area-level models. Such models assist transport planners to efficiently monitor network usage, to identify opportunities to enhance safety and to evaluate the impact of policy and infrastructure interventions. However, given the sparsity and scarcity of bicycling data as compared to its motorised counterparts, link-level bicycling volume estimation literature is relatively limited. This paper conducts a scoping review of link-level bicycling volume estimation methods by implementing systematic search strategies across relevant databases, thereby identifying appropriate studies for the review. The review resulted in some interesting findings. Among all the methods implemented, direct demand modelling was the predominant one. Not a single study implemented multiple modelling approaches in the same study area, thereby not allowing for comparison of these approaches. Most studies were conducted in the United States. It was also observed that there exists a lot of heterogeneity in the reporting of basic study characteristics and validation results, sometimes to the extent of not reporting these at all. The study presents the different types of data used in modelling (count, travel survey, GPS data) along with an array of popular explanatory variables that can inform future studies about data collection and variable selection for modelling. The study discusses the strengths and limitations of different methods and finally presents recommendations for future research. [ABSTRACT FROM AUTHOR]

The most frustrating outcome of an SEM analysis is nonconvergence. Nonconvergence typically happens when the sample size is small ( N < 100) or very small ( N < 50). To minimize the frequency of nonconvergence, this paper proposes a solution called bounded estimation. The idea is to use data-driven lower and upper bounds for a subset of the model parameters during estimation. In this paper, we provide a rationale to compute these bounds, and we study the effect of different approaches to employ these bounds on the frequency of nonconvergence. A simulation study shows that bounded estimation dramatically decreases the frequency of nonconvergence in both correctly and misspecified models, without any (negative) effects on the quality of the point estimates for the unbounded parameters. [ABSTRACT FROM AUTHOR]

Abstract Likewise the logistic model and the probit model is often used to consider the associations between a dichotomous dependent variable and some covariates. The probit model is a model that has a lot of important and engaging applications in practice especially in economics and finance. In the framework paper, we propose the Pearson chi-squared (PSC) and unweighted residual sum of square (URSS) tests of fit for a probit model, to enrich and diversify the topic of goodness of fit (GOF) test for regression models. This is a very interesting topic in theory and has enormous practical applications. Theoretically, we have proved that both the proposed approaches are asymptotic to the standard normal distribution. In terms of numerical research and practical applications, several simulations and a real-life data set a fishing data set are investigated in this work. The obtained results in these parts also help to illustrate numerical that the proposed formulas are very reliable. Besides, the findings in the empirical analysis are very consistent with reality. It will be very meaningful evidence to illustrate to everyone how to fish to get the most amount of fish while fishing. Finally, some discussions, conclusions, and future work are also included in this study. [ABSTRACT FROM AUTHOR]

Although the surface energy budget is essential to determine Earth's climate, site measurements of various radiative components are still too scarce to properly characterize their spatial and temporal variations. This has led to the development of a growing number of surface radiation products, mainly including remotely sensed data, model reanalysis data, and simulations using General Circulation Models (GCMs). This collection of papers introduces new techniques, including the use of machine learning methods for radiation estimation, and evaluates and compares various radiation products, as well as their spatio-temporal variations. These studies show large discrepancies among various products across nearly all radiative parameters in either accuracy or spatio-temporal variations. However, remotely sensed radiation products perform relatively better than others. Despite this, there is an urgent need for further efforts to address these discrepancies and improve the accuracy of these estimates. Even though the major radiative parameters including downward shortwave radiation, net longwave radiation, and albedo, from most products show insignificant long-term variation trends on a global scale, only specific regions, such as the Yunnan-Kweichow Plateau (YKP) and regions with permafrost (i.e. Qinghai-Tibet Plateau and Arctic) and glaciers (i.e. Altai Mountains) exhibit remarkable trends. [ABSTRACT FROM AUTHOR]

Sparse unmixing is a semisupervised unmixing method based on the linear mixture model, in which the spectral library is known a prior, and has received considerable attention recently. It has been confirmed that the spatial information in hyperspectral images plays a crucial role in improving the performance of sparse unmixing algorithms. However, the spatial information extracted or captured in most unmixing algorithms is inaccurate and robust enough, which leads to artificial block noise or outliers in the estimated abundance maps, especially as the noise level increases. To address these problems and more efficiently utilize the spatial information, this paper proposes a graph learning and denoising-based weighted sparse mixing (GLDWSU) algorithm, which includes three stages in the unmixing procedure: graph learning, denoising and unmixing. In the first stage, the graph Laplacian matrix is adaptively learned to capture the spatial structure of HSI with the relative total variation (RTV) regularization. In the next stage, HSI is denoised with the learned Laplacian matrix by using Laplacian smoothing. In the final stage, the denoised HSI is unmixed with the reweighted-norm regularization based on the alternating direction method of multipliers (ADMM) framework. The experimental results on both simulated and real data sets show that the proposed GLDWSU algorithm can more accurately capture and utilize the spatial structure information of HSI with a low computational cost and outperforms all the compared methods. [ABSTRACT FROM AUTHOR]

Modeling of solar photovoltaic cell is an essential requirement in the computations involved in solar photovoltaic power systems. Some metaheuristic algorithms are used for determining the cell parameters in the literature, however, more investigation is required with reference to varying solar irradiation and temperature to improve the accuracy of the models. Hence, this paper proposes firefly algorithm for identification of the cell parameters accurate enough to construct the cell characteristics under varying solar irradiation and temperature conditions. Experimental results obtained at standard irradiation and temperature of 1000 W/m2, 25°C, and at other irradiation levels such as 80 0 W/m2 and 600 W/m2, temperature levels such as 40°C and 50°C were presented along with simulated values. The value of series resistance, shunt resistance and diode ideality factor for temperatures from 20°C to 60°C and irradiation of 400 W/m2 to 1000 W/m2 are computed using this proposed method. A comparison of the proposed method with other researchers at irradiation of 1000, 800, and 600 W/m2 and 25°C was provided. The results of implementation show that there is a good agreement between computed values and data sheet values. The proposed method will definitely be useful for large scale solar photovoltaic designers, researchers, simulators. [ABSTRACT FROM AUTHOR]

In this paper, we point-out a major error in the proofs of the main results of Bahadır Yüzbaşı, Yasin Asar & S. Ejaz Ahmed [(2022). Liu-type shrinkage estimations in linear models, Statistics, 56:2, 396–420]. In particular, the proofs of their Theorems 3.4–3.5 are based on their Lemma 3.2 which is incorrect. [ABSTRACT FROM AUTHOR]

The group testing procedure divides sampled units into several groups, and then obtains an overall test result for each group. It is used to identify specific individuals who have a given attribute, or to estimate the overall prevalence of the attribute in the population. We here investigate how group retesting can improve precision of estimation and its cost-efficiency, which are important considerations for investigators. Retesting uses two or more group stages, with repeat testing of the original samples at each stage. Previous authors have proposed a procedure with two stages having equal group sizes, and where the number of groups tested at the second stage is based on the number of positive groups in the first stage. In this paper, our main focus is on estimating the prevalence p of affected individuals in a population, and identifying cost-efficient experimental designs, when using two-stage testing with unequal group sizes at each stage. We use analytical solutions for the precision of estimation, together with simulations to evaluate various experimental designs. We consider the value of retesting at the second stage, and determine when using only one stage of testing might be sufficiently precise. [ABSTRACT FROM AUTHOR]

In empirical finance, it is usual to consider holidays as if they do not exist. The main goal of this paper is to assess the effects of holidays on volatility estimation and prediction. Holidays are taken into account by assuming they are missing values in a time series of returns generated by a Stochastic volatility (SV) model. Estimation is evaluated through Monte Carlo experiments. In addition, we assess the effects of holidays on one-step ahead Value-at-Risk forecasting using several time series returns. The results are slightly better when we take into account the missing values, especially for VaR forecasting. [ABSTRACT FROM AUTHOR]

Acknowledging evidence that the ability to estimate has major consequences for both later mathematics learning and real-world functionality, this paper examines the national mathematics curriculum for compulsory school for each of Denmark, Norway and Sweden for the estimation-related opportunities it offers children. Framed against four conceptually and procedurally different forms of estimation (computational, measurement, quantity and number line), each of which is implicated differently in the later learning of mathematics, analyses indicated that none of the four forms of estimation were addressed explicitly in the Norwegian curriculum. Expectations of computational and measurement estimation were present in both the Danish and the Swedish curricula, although neither referred to either quantity or number line estimation. Even when estimation-related learning outcomes were articulated, there was no evidence of the processes by which they might be realised. Finally, there were no acknowledgements that estimation may contribute to the learning of other mathematical topics. [ABSTRACT FROM AUTHOR]

The most widely used approach for reliability estimation is the well-known stress-strength model, θ = P(X < Y), where X and Y are random variables. In this model, the reliability, θ, of the system is the probability that the system is strong enough to overcome the stress imposed on it. In most cases, X and Y are assumed to be independent. Nevertheless, in reality, the strength variable Y could be highly dependent on the stress variable X. In this paper, we discuss the kernel-based estimation of θ when X and Y are dependent random variables under progressive type II censored sample. The asymptotic properties of the kernel-based estimators of θ based on progressive type II censoring are proposed. An extensive computer simulation is conducted to gain insight into the performance of the proposed estimators. A real data example is provided to illustrate the process. [ABSTRACT FROM AUTHOR]

This research note is prompted by a paper by Kashyap (Is prenatal sex selection associated with lower female child mortality? Population Studies 73(1): 57–78). Kashyap's paper, which provides 40 original estimates of missing female births, relies on an alternative definition of missing female births, leading to estimates of about half the magnitude of other estimates. There appears, therefore, a real need to take stock of the concept of missing female births widely used by statisticians around the world for assessing the demographic consequences of prenatal sex selection. This research note starts with a brief review of the history of the concept and the difference between Amartya Sen's original method and the alternative method found elsewhere to compute missing female births. We then put forward three different arguments (deterministic and probabilistic approaches, and consistency analysis) in support of the original computation procedure based on the number of observed male births and the expected sex ratio at birth. [ABSTRACT FROM AUTHOR]

This paper describes the point and interval estimation of the unknown parameters of Kumaraswamy (Ku) distribution under the adaptive Type-II hybrid progressive censored samples. First, we obtain the maximum likelihood estimation (MLE) of the parameters using Newton-Raphson (NR) method, expectation maximization (EM) and stochastic EM (SEM) algorithms. In addition, we derive the asymptotic distribution of the parameters and the asymptotic confidence intervals. Moreover, two bootstrap confidence intervals are achieved. Second, the Bayesian estimation of the parameters is approximated by using the Markov Chain Monte Carlo (MCMC) algorithm and Lindley's method due to the lack of explicit forms. Furthermore, the highest posterior density (HPD) credible intervals of the parameters are derived. Finally, the different proposed estimations have been compared by the simulation studies and a practical data set is analyzed to illustrative aims. [ABSTRACT FROM AUTHOR]

In solar energy applications, a complete knowledge and detailed analysis of the solar radiation potential is a prerequisite. In this paper, two different photovoltaic (PV) potential analysis approaches are compared. The first approach is based on a simulation of PV array and six different solar radiation estimation models. The outputs of these models are compared with hourly ground measured data by using statistical error methods. The most suitable solar radiation model which gives more accurate results is used to calculate the PV potential of Engineering Faculty of Eskişehir Technical University. The PV panel simulation is implemented in Matlab/Simulink besides that a suitable algorithm is selected to calculate the possible amount of PV panel energy generation based on the hourly measured wind speed (WS), air pressure, global solar radiation (GSR), and temperature values. In the second approach, building roof surface PV potential of the selected area is calculated and the available roof area for PV installation is determined by using Aeronautical Geographical Information System (ArcGIS) software. When the performances of the methods are compared, these methods obtained satisfactory results. Furthermore, it is clear that the algorithms and effective factors of the two methods are different. The results of ArcGIS outperform the PV array simulation based on Badescu model by the reason of its suitability of selecting optimal site for PV installation. [ABSTRACT FROM AUTHOR]

This paper deals with the analysis of the random process of cyclic loading of a mining belt conveyor with portions of ore discharged by loaders or trucks. Such transfer of transported ore from a cyclic to a continuous transport is typical for the specific mining operations implemented in the underground copper ore mines with room and pillar mining. The conveyors in such systems are usually significantly oversized in order to match the peak loads of cumulated discharges of ore hauled from mining fields by loaders. Therefore, the actual loadings that occur in the mining transportation systems need to be analysed to provide the data for more accurate design and control of belt conveyors. The large data-set of actual loadings of the belt conveyors has been used for the stochastic modelling of the analysed process. The compound Poisson process has been proposed as mathematical tool to analyse/describe properties of ore flow. The discussion of the chosen distribution functions and results of the fitted model simulations compared with the examined measurement data are presented. In this paper, the case of a belt conveyor loaded only by a single feeding point where loaders are randomly discharged have been analysed. More complex cases (several loading points, mixed ore supply from cyclic and continuous ore mass flow from preceding conveyors or ore bunkers) are under investigation and will be presented in the future. [ABSTRACT FROM AUTHOR]

Data analysis plays a key role in supporting the development of sustainable transportation. Using the large-scale household mobility survey data collected in Milan, Italy during 2005-2006, we study whether the large-scale data contribute to improving accuracy in estimating household travel modes. This paper presents three machine learning methods including multinomial logit (MNL) model, random forest (RF) and support vector machine (SVM) to estimate the household travel mode. Their model accuracies are 70.41%, 71.89%, 72.74% respectively under the full sample size. It is found that the accuracies of these three methods fluctuate fiercely when the sample size is less than 20,000 and then stabilize gradually with continuous increasing it. After stabilization occurs, accuracies with these three methods do not significantly increase as the sample size continues to increase. We also study the travel characteristics derived from the large-scale survey data, which is fundamental for developing a sustainable transportation system. The collected data items include five explanatory variables, i.e., household size (HS), vehicle ownership, household income (HI), travel distance, travel time and one response variable (i.e., household travel mode), which includes public transport (PT), private car, usage of PT and private car simultaneously and the others travel modes (e.g., walk). We further investigate the importance of explanatory variables in terms of estimating household travel mode choice with the MNL model. It is found that vehicle ownership is the most critical factor influencing household travel mode choice, followed by travel distance, travel time, HS and HI. The ranking result is consistent with the RF approach. [ABSTRACT FROM AUTHOR]

Data analysis plays a key role in supporting the development of sustainable transportation. Using the large-scale household mobility survey data collected in Milan, Italy during 2005–2006, we study whether the large-scale data contribute to improving accuracy in estimating household travel modes. This paper presents three machine learning methods including multinomial logit (MNL) model, random forest (RF) and support vector machine (SVM) to estimate the household travel mode. Their model accuracies are 70.41%, 71.89%, 72.74% respectively under the full sample size. It is found that the accuracies of these three methods fluctuate fiercely when the sample size is less than 20,000 and then stabilize gradually with continuous increasing it. After stabilization occurs, accuracies with these three methods do not significantly increase as the sample size continues to increase. We also study the travel characteristics derived from the large-scale survey data, which is fundamental for developing a sustainable transportation system. The collected data items include five explanatory variables, i.e., household size (HS), vehicle ownership, household income (HI), travel distance, travel time and one response variable (i.e., household travel mode), which includes public transport (PT), private car, usage of PT and private car simultaneously and the others travel modes (e.g., walk). We further investigate the importance of explanatory variables in terms of estimating household travel mode choice with the MNL model. It is found that vehicle ownership is the most critical factor influencing household travel mode choice, followed by travel distance, travel time, HS and HI. The ranking result is consistent with the RF approach. [ABSTRACT FROM AUTHOR]

This paper presents an Artificial Neural Network based algorithm to estimate the sideslip angle of a vehicle in different road conditions. The method is based on two parallel tasks: (a) estimation of the sideslip angle in three road conditions, namely dry, wet and icy, by means of three separate regression networks, and (b) identification of the road condition with a pattern recognition classifier allowing to select the correct estimation output among the three regression network outputs. The regression and classification networks are trained by means of datasets recorded during driving sessions conducted on a high-performance instrumented vehicle in the three road conditions and with different maneuvers and driving cycles. The algorithm has been deployed on the same vehicle to test the performance on the real application. The results are presented in terms of comparison between the estimated angle and its direct measurement obtained by a high precision optical sensor installed onboard. The resulting accuracy of the method is about 98.6%. [ABSTRACT FROM AUTHOR]

In this paper, we develop a new discrete bivariate distribution, i.e., the weighted bivariate geometric distribution whose marginals are univariate weighted geometric distributions and show that the proposed distribution is more flexible and applicable than the classical bivariate geometric distribution, which is in fact a special case of the proposed model, and the bivariate Freund distributions. We shall determine several important distributional and reliability characteristics of the distributions. Further, we shall develop classical and Bayesian inferences for the unknown parameters. Extensive Monte Carlo simulations and analysis of a real data set are conducted. [ABSTRACT FROM AUTHOR]

This paper studies the properties of a particular estimator of the fractal index of a time series with a view to applications in financial econometrics and mathematical finance. We show how measurement noise (e.g., microstructure noise) in the observations will bias the estimator, potentially resulting in the econometrician erroneously finding evidence of fractal characteristics in a time series. We propose a new estimator which is robust to such noise and construct a formal hypothesis test for the presence of noise in the observations. A number of simulation exercises are carried out, providing guidance for implementation of the theory. Finally, the methods are illustrated on two empirical data sets; one of turbulent velocity flows and one of financial prices. [ABSTRACT FROM AUTHOR]

This paper propounds the use of 'Weights' method to assess the relative importance of input features for estimating 10-min global solar irradiation on an arbitrary tilted plane, using artificial Neural Networks (ANNs). Two different data sources are exerted: satellite data, including different inclinations, are used for learning, while ground data, measured at Bouzaréah radiometric station (Algeria), are used for testing. 'Weights' method allowed to identify the relative importance of input variables and rank them according to their order of contribution, thus significantly improving the performance of the model by eliminating the parameters that are poorly correlated with the network output. The results show that the most relevant input data are in the following order: the global horizontal irradiation 35.9%, extraterrestrial irradiation 25.08%, declination 15.81%, elevation of the sun 12.60% and inclination 10.61%. The optimal configuration includes 16 neurons in the hidden layer with nRMSE = 6.37% and R2 = 0.992. [ABSTRACT FROM AUTHOR]

The Own Children Method (OCM) is an indirect procedure for deriving age-specific fertility rates and total fertility from children living with their mothers at a census or survey. The method was designed primarily for the calculation of overall fertility, although there are variants that allow the calculation of marital fertility. In this paper we argue that the standard variants for calculating marital fertility can produce misleading results and require strong assumptions, particularly when applied to social or spatial subgroups. We present two new variants of the method for calculating marital fertility: the first of these allows for the presence of non-marital fertility and the second also permits the more robust calculation of rates for social subgroups of the population. We illustrate and test these using full-count census data for England and Wales in 1911. [ABSTRACT FROM AUTHOR]

This paper deals with the problem of estimating the Minimum Initial Marking (MIM) of Labeled Petri Nets (L-PN). By the observation of a sequence of labels, we determine the set of possible MIMs related to a given L-PN through an approach based on GRASP (Greedy Randomized Adaptive Search Procedure) inspired method – GMIM. The objective is to get the maximum of feasible MIMs by exploring the search space and giving best solutions for real time cyber systems in short time. We consider four basic assumptions during the reasoning: (i) the L-PN structure is known; (ii) for each transition of L-PN, a label is associated, (iii) the label sequence is known, and (iv) all transitions of L-PN are observable. We show the validity and efficiency of our approach by applying the proposed GMIM metaheuristic to two validation examples: Initialization of two parallel machines (example widely cited in literature) and resources allocation in a monitoring problem via mobile robot network. [ABSTRACT FROM AUTHOR]

In this paper, we use convolutional neural networks to find the Hölder exponent of simulated sample paths of the rBergomi model, a recently proposed stock price model used in mathematical finance. We contextualise this as a calibration problem, thereby providing a very practical and useful application. [ABSTRACT FROM AUTHOR]

This paper proposes a new generator function based on the inverted Kumaraswamy distribution and introduces 'generalized inverted Kumaraswamy-G' family of distributions. We provide a comprehensive account of some of its mathematical properties that include the ordinary and incomplete moments, quantile and generating functions and order statistics. The infinite mixture representations for probability density and cumulative distribution and entropy functions of the new family are also established. The density function of the ith-order statistics is expressed as an infinite linear combination of baseline densities and model parameters are estimated by maximum likelihood method. Four special models of this family are also derived along with their respective hazard rate functions. The maximum likelihood estimation (MLE) method is used to obtain the model parameters. Monte Carlo simulation experiments are executed to assess the performance of the ML estimators under the corresponding generated models while some data applications are also illustrated. The results of the study show that the proposed distribution is more flexible as compared to the baseline model. This distribution especially can be used to model symmetric, left-skewed, right-skewed and reversed-J data sets. [ABSTRACT FROM AUTHOR]

In this paper, we establish a mathematical model with two delays to reflect the intrinsic and extrinsic incubation periods of virus in dengue transmission. The basic reproduction number R 0 of the model is defined. It is proved that the disease-free equilibrium is stable when R 0 < 1 and the positive equilibrium is stable when R 0 > 1. Next, we derive an estimation formula for the reproduction number R t when the human population is partially susceptible to dengue. As an application, the R t values of dengue transmission in Singapore in the years 2013–2015 are estimated. Our estimation method can be applied to estimating R t of other infectious diseases, especially when the human population is not completely susceptible to the disease. [ABSTRACT FROM AUTHOR]

This paper provides an extension for "sequential order statistics" (SOS) introduced by Kamps. It is called "developed sequential order statistics" (DSOS) and is useful for describing lifetimes of engineering systems when component lifetimes are dependent. Explicit expressions for the joint density function, the marginal distributions and the means of DSOS are derived. Under the well known "conditional proportional hazard rate" (CPHR) model and the Gumbel families of copulas for dependency among component lifetimes, some findings are reported. For example, it is proved that the joint density functions of DSOS and SOS have the same structure. Various illustrative examples are also given. [ABSTRACT FROM AUTHOR]

In this paper, we introduce a new class of (probability) distributions, based on a cosine-sine transformation, obtained by compounding a baseline distribution with cosine and sine functions. Some of its properties are explored. A special focus is given to a particular cosine-sine transformation using the exponential distribution as baseline. Estimations of parameters of a particular cosine-sine exponential distribution are performed via the maximum likelihood estimation method. A simulation study investigates the performances of these estimates. Applications are given for four real data sets, showing a better fit in comparison to some existing distributions based on some goodness-of-fit tests. [ABSTRACT FROM AUTHOR]

This paper presents a new stationary integer-valued bilinear time series model of the first order by mixing the thinning and Pegram operators. Some statistical properties of the model are obtained, involving the conditional moments, autocovariance and spectral density function. Estimation of the model parameters is discussed using the Yule-Walker and conditional least squares methods with a simulation study for evaluating the performance of those estimators. Applicability of the process is investigated using a practical count data set with comparing the model to a competitive bilinear model using some marginal distributions of innovations. Issue of forecasting data is discussed under the proposed model. [ABSTRACT FROM AUTHOR]

In this paper , a new discrete two–parameter distribution α ∈ ℜ − {0} and 0 < θ < 1, the Geometric ArcTan (GAT) distribution is introduced. The geometric distribution is a limiting case of this model when α tends to zero. Similarly to the the latter distribution, this probabilistic family is unimodal but the mode can be located at zero or in other point of the support. Then, after deriving some of its more relevant properties , the issue of parameter investigation is investigated. Next, the GAT distribution is used to explain the demand for health services by means of a regression model. Numerical results show that this new model outperforms the negative binomial distribution. [ABSTRACT FROM AUTHOR]

Autoregressive Hilbertian (ARH) processes are of great importance in the analysis of functional time series data and estimation of the autocorrelation operators attracts the attention of various researchers. In this paper, we study estimators of the autocorrelation operators of periodically correlated autoregressive Hilbertian processes of order one (PCARH(1)), which is an extension of ARH(1) processes. The estimation method is based on the spectral decomposition of the covariance operator and considers two main cases: known and unknown eigenvectors. We show the consistency in the mean integrated quadratic sense of the estimators of the autocorrelation operators and present upper bounds for the corresponding rates. [ABSTRACT FROM AUTHOR]

This paper shows the importance of the use of Monte Carlo experiments within Simple Linear Regression (SLR) Models through Refined Descriptive Sampling and proves practically that the asymptotic theory of the Ordinary Least Squares (OLS) estimator still hold with small samples when the Normality errors assumption is released. To this end, a simple Linear Regression adaptive Refined Descriptive Sampling (L2RDS) algorithm is proposed to estimate the parameter of SLR models by OLS method and computes its properties. Real data are used to properly specified the simulation model. The results show that L2RDS algorithm provides accurate and efficient point estimates. [ABSTRACT FROM AUTHOR]