This paper proposes several methodologies whose objective consists of securing copula density estimates. More specifically, this aim will be achieved by differentiating bivariate least-squares polynomials fitted to Deheuvels’ empirical copulas,...
The Cochran-Mantel-Haenszel (CMH) and nonparametric analysis of variance (NP ANOVA) methodologies are both sets of tests for categorical response data. The latter are competitor tests for the ordinal CMH tests in which the response variable is necess...
Due to a broad consensus in the engaging of global economic integrations, host countries encounter a number of challenges, especially in international capital mobility. Foreign direct investment (FDI) becomes a pillar for economic development. This s...
A completely nonparametric method for the estimation of mixture cure models is proposed. Nonparametric estimators for the cure probability (incidence) and for the survival function of the uncured population (latency) are introduced. In addition, a bo...
In ultrahigh dimensional data analysis, to keep computational performance well and good statistical properties still working, nonparametric additive models face increasing challenges. To overcome them, we introduce a methodology of model selection fo...
In a stress-strength system, the mean remaining strength is the key to deciding the safety threshold for the system continuing to operate. In this study, a multi-state stress-strength system composed of two-state components is discussed, and the mean...
This paper proposes a methodology to examine economies of scope using the recent order-α nonparametric method. It allows us to investigate economies of scope by comparing the efficient order-α frontiers of firms that produce two or more goods with th...
To better fit the actual data, this paper will consider both spatio-temporal correlation and heterogeneity to build the model. In order to overcome the “curse of dimensionality” problem in the nonparametric method, we improve the estimati...
The study focuses on the application of a nonparametric methodology for evaluating the sustainability of retrofitting interventions to be applied on different typologies of buildings and different climate zones of the Mediterranean area. The paper st...
Under the Bayesian framework, this study proposes a Tweedie compound Poisson partial linear mixed model on the basis of Bayesian P-spline approximation to nonparametric function for longitudinal semicontinuous data in the presence of nonignorable mis...
The kriging methodology can be applied to predict the value of a spatial variable at an unsampled location, from the available spatial data. Furthermore, additional information from secondary variables, correlated with the target one, can be included...
This paper compares two deconvolution methodologies used to estimate residence time distributions (RTD) in industrial closed-circuit ball mills. Parametric and non-parametric deconvolution techniques were evaluated. Both techniques allowed for direct...
This article introduces new counterfactual standardization techniques for comparing duration distributions subject to random censoring through counterfactual decompositions. The counterfactual distribution of one population relative to another is com...
This paper describes the non-parametric shape optimization process for a football boot bottom plate. The non-parametric shape optimization process changes the nodes’ location of a model and outputs an optimum shape, which satisfies an optimizat...
In classical survival analysis, it is assumed that all the individuals will experience the event of interest. However, if there is a proportion of subjects who will never experience the event, then a standard survival approach is not appropriate, and...
We study the non-parametric estimation of partially linear generalized single-index functional models, where the systematic component of the model has a flexible functional semi-parametric form with a general link function. We suggest an efficient an...
We propose a new methodology to transform a time series into an ordered sequence of any entropic and information functionals, providing a novel tool for data analysis. To achieve this, a new algorithm has been designed to optimize the Probability Den...
This paper introduces a non-parametric methodology based on classical unsupervised clustering techniques to automatically identify the main regions of a space, without requiring the objective number of clusters, so as to identify the major regular st...
Actigraphy quantifies human locomotor activity by measuring wrist acceleration via wearable devices at relatively high rates and converting it into lower-temporal-resolution activity values; however, the computational implementations of this data com...
The degradation of biopolymers such as polylactic acid (PLA) has been studied for several years; however, the results regarding the mechanism of degradation are not completely understood yet. PLA is easily processed by traditional techniques includin...
In this paper, we describe a new framework to combine experts’ judgments for the prevention of driving risks in a cabin truck. In addition, the methodology shows how to choose among the experts the one whose predictions fit best the environmental con...
Remote sensing and advanced statistical models have been used to assess actual biomass and to analyze the biomass growth in a mature forest of Pinus halepensis Mill. Biomass maps using Landsat-5 in 2011 and Landsat-8 imagery in 2017 and non-parametri...
The objective of this work is to present a methodology that automates the prediction of students’ academic performance at the end of the course using data recorded in the first tasks of the academic year. Analyzing early student records is helpful in...
In unveiling the non-parametric estimation of the conditional hazard function through the local linear method, our study yields key insights into the method’s behavior. We present rigorous analyses demonstrating the mean square convergence of t...
Single-index models are potentially important tools for multivariate non-parametric regression analysis. They generalize linear regression models by replacing the linear combination
Zhang in 2012 introduced a nonparametric estimator of Shannon’s entropy, whose bias decays exponentially fast when the alphabet is finite. We propose a methodology to estimate the bias of this estimator. We then use it to construct a new estimator of...
Interval-censored survival data arise frequently in biomedical and epidemiological studies where event times are observed only within observation intervals. Classical non-parametric estimators, such as the Kaplan–Meier (KM) estimator under impu...
This paper presents a study on dynamic line rating (DLR) forecasting procedure aimed at developing a new methodology able to forecast future ampacity values for rare and extreme events. This is motivated by the belief that to apply DLR network operat...
The purpose of this note is to introduce and investigate the nonparametric estimation of the conditional mode using wavelet methods. We propose a new linear wavelet estimator for this problem. The estimator is constructed by combining a specific rati...
This paper discusses some methodological aspects of the historical analysis of drought, particularly the trend assessment. The Standardized Evapotranspiration Index (SPEI) is widely used as a measure of drought condition. Since different SPEI thresho...
Sign, Wilcoxon and Mann-Whitney tests are nonparametric methods in one or two-sample problems. The nonparametric methods are alternatives used for testing hypothesis when the standard methods based on the Gaussianity assumption are not suitable to be...
4 September 2023
In the present study, the introduction of the Innovative Polygon Trend Analysis (IPTA), a non-parametric trend analysis methodology, is proposed as an alternative to the classical trend analysis methodology, according to which processes (normality, i...
Investments are a demanding and, at the same time, threatening indicator for enterprises not only in the time of depression such as the current coronavirus pandemic but also under normal market conditions. The goal of this article is to propose a met...
Remotely sensed data are essential for understanding environmental dynamics, for their forecasting, and for early detection of disasters. Microwave remote sensing sensors complement the information provided by observations in the optical spectrum, wi...
This study aimed to introduce non-parametric tests and guard bands to assess the compliance of some river water properties with Brazilian environmental regulations. Due to the heterogeneity of the measurands pH, Biochemical Oxygen Demand (BOD), manga...
Handgrip strength (HGS) is widely used as a biomarker of muscle function and overall health in older adults. However, conventional analyses based on peak force values may overlook relevant temporal features of the HGS curve. This cross-sectional stud...
Entropy and extropy are emerging concepts in machine learning and computer science. Within the past decade, statisticians have created estimators for these measures. However, associated variability metrics, specifically varentropy and varextropy, hav...
We address the problem of non-parametric estimation of the recently proposed measures of statistical dispersion of positive continuous random variables. The measures are based on the concepts of differential entropy and Fisher information and describ...
This paper presents a methodology to calculate day-ahead wind speed predictions based on historical measurements done by weather stations. The methodology was tested for three locations: Colombia, Ecuador, and Spain. The data is input into the proces...
In this work, a novel multimodal learning approach for early prediction of birth weight is presented. Fetal weight is one of the most relevant indicators in the assessment of fetal health status. The aim is to predict early birth weight using multimo...
This study introduces a non-parametric methodology for estimating expected frequencies of defaults and other credit events. The methodology allows for an independent estimation of a credit-quality variable, referred to as a default rank variable. In...
In this article, a methodology is developed to solve an interval and a fractional interval programming problem by converting into a non-interval form for second order cone constraints, with the objective function and constraints being interval valued...
Previous studies have demonstrated that non-parametric hedging models using temperature derivatives are highly effective in hedging profit/loss fluctuation risks for electric utilities. Aiming for the practical applications of these methods, this stu...
This study examines the impact of environmental regulation on the Singapore stock market using the event study methodology. Several asset pricing models are used to estimate sectoral abnormal returns. Additionally, we estimate the change in systemati...
Singular spectrum analysis (SSA) is a non-parametric method that breaks down a time series into a set of components that can be interpreted and grouped as trend, periodicity, and noise, emphasizing the separability of the underlying components and se...
The growing discourse surrounding biomass energy’s environmental ramifications has ignited debate among policymakers. While biomass remains a primary and readily accessible energy source, various studies have extensively examined its implicatio...
The aim of this work was to answer the following question: can influential points modify the recommendation of genotypes, based on regression methods, in the presence of genotype × environment (G × E)? Therefore, we compared the parameters of the ada...
The Water Framework Directive (WFD) requires EU member states to assess the chemical status of groundwater bodies, a status defined according to threshold values for harmful elements and based on/the natural background level (NBL). The NBL is defined...
Data Envelopment Analysis (DEA) is a non-parametric method for evaluating the efficiency of Decision Making Units (DMUs) with multiple inputs and outputs. In the traditional DEA models, the DMU is allowed to use its most favorable multiplier weights...
Identifying the mutational processes that shape the nucleotide composition of the mitochondrial genome (mtDNA) is fundamental to better understand how these genomes evolve. Several methods have been proposed to analyze DNA sequence nucleotide composi...
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