Quantile Research Articles

A New Extension of the Kumaraswamy Exponential Model with Modeling of Food Chain Data

Statistical models are useful in explaining and forecasting real-world occurrences. Various extended distributions have been widely employed for modeling data in a variety of fields throughout the last few decades. In this article we introduce a new extension of the Kumaraswamy exponential (KE) model called the Kavya–Manoharan KE (KMKE) distribution. Some statistical and computational features of the KMKE distribution including the quantile (QUA) function, moments (MOms), incomplete MOms (INMOms), conditional MOms (COMOms) and MOm generating functions are computed. Classical maximum likelihood and Bayesian estimation approaches are employed to estimate the parameters of the KMKE model. The simulation experiment examines the accuracy of the model parameters by employing Bayesian and maximum likelihood estimation methods. We utilize two real datasets related to food chain data in this work to demonstrate the importance and flexibility of the proposed model. The new KMKE proposed distribution is very flexible, more so than numerous well-known distributions.

Modeling to Factor Productivity of the United Kingdom Food Chain: Using a New Lifetime-Generated Family of Distributions

This article proposes a new lifetime-generated family of distributions called the sine-exponentiated Weibull-H (SEW-H) family, which is derived from two well-established families of distributions of entirely different nature: the sine-G (S-G) and the exponentiated Weibull-H (EW-H) families. Three new special models of this family include the sine-exponentiated Weibull exponential (SEWEx), the sine-exponentiated Weibull Rayleigh (SEWR) and sine-exponentiated Weibull Burr X (SEWBX) distributions. The useful expansions of the probability density function (pdf) and cumulative distribution function (cdf) are derived. Statistical properties are obtained, including quantiles (QU), moments (MO), incomplete MO (IMO), and order statistics (OS) are computed. Six numerous methods of estimation are produced to estimate the parameters: maximum likelihood (ML), least-square (LS), a maximum product of spacing (MPRSP), weighted LS (WLS), Cramér–von Mises (CRVM), and Anderson–Darling (AD). The performance of the estimation approaches is investigated using Monte Carlo simulations. The total factor productivity (TFP) of the United Kingdom food chain is an indication of the efficiency and competitiveness of the food sector in the United Kingdom. TFP growth suggests that the industry is becoming more efficient. If TFP of the food chain in the United Kingdom grows more rapidly than in other nations, it suggests that the sector is becoming more competitive. TFP, also known as multi-factor productivity in economic theory, estimates the fraction of output that cannot be explained by traditionally measured inputs of labor and capital employed in production. In this paper, we use five real datasets to show the relevance and flexibility of the suggested family. The first dataset represents the United Kingdom food chain from 2000 to 2019, whereas the second dataset represents the food and drink wholesaling in the United Kingdom from 2000 to 2019 as one factor of FTP; the third dataset contains the tensile strength of single carbon fibers (in GPa); the fourth dataset is often called the breaking stress of carbon fiber dataset; the fifth dataset represents the TFP growth of agricultural production for thirty-seven African countries from 2001–2010. The new suggested distribution is very flexible and it outperforms many known distributions.

Mixed Topp-Leone-Kumaraswamy distribution

In this article, a new generalization of the Topp-Leone distribution with a unit interval, namely Mixed Topp-Leone-Kumaraswamy distribution is defined and studied. The mathematical properties of this mixing distribution are described. Moments, quantile function, R?nyi entropy, incomplete moments and moments of residual are obtained for the new Mixed Topp-Leone - Kumaraswamy distribution. The maximum likelihood (MLE), Crans (CM) , Percentile (PM) and Particle Swarm Optimization(PSO) estimators of the parameters are derived. The percentile Method is more efficient method as compred to the others. Two real data sets are used to illustrate an application and superiority of the proposed distribution.

Static and dynamic quantile preferences

Static and dynamic quantile preferences

A Study on the Adequacy of National Health Insurance Premium Quantiles as the Basis for Determining Income Level

A Study on the Adequacy of National Health Insurance Premium Quantiles as the Basis for Determining Income Level

The Kumaraswamy Generalized Half-Normal Distribution for Skewed Positive Data

For the first time, we propose and study the Kumaraswamy generalized half-normal distribution for modeling skewed positive data. The half-normal and generalized half-normal (Cooray and Ananda, 2008) distributions are special cases of the new model. Various of its structural properties are derived, including explicit expressions for the density function, moments, generating and quantile functions, mean deviations and moments of the order statistics. We investigate maximum likelihood estimation of the parameters and derive the expected information matrix. The proposed model is modified to open the possibility that long-term survivors may be presented in the data. Its applicability is illustrated by means of four real data sets.

Some Reliability Properties of Extropy and its Related Measures Using Quantile Function

Extropy is a recent addition to the family of information measures as a complementary dual of Shannon entropy, to measure the uncertainty contained in a probability distribution of a random variable. A probability distribution can be specified either in terms of the distribution function or by the quantile function. In many applied works, there do not have any tractable distribution function but the quantile function exists, where a study on the quantile-based extropy are of importance. The present paper thus focuses on deriving some properties of extropy and its related measures using quantile function. Some ordering relations of quantile-based residual extropy are presented. We also introduce the quantile-based extropy of order statistics and cumulative extropy and studied its properties. Some applications of empirical estimation of quantile-based extropy using simulation and real data analysis are investigated.

On the Generalized Odd Transmuted Two-Sided Class of Distributions

In this paper, a general class of two-sided lifetime distributions is introduced via odd ratio function, the well-known concept in survival analysis and reliability engineering. Some statistical and reliability properties including survival function, quantiles, moments function, asymptotic and maximum likelihood estimation are provided in a general setting. A special case of this new family is taken up by considering the exponential model as the parent distribution. Some characteristics of this specialized model and also a discussion associated with survival regression are provided.A simulation study is presented to investigate the bias and mean square error of the maximum likelihood estimators. Moreover, two examples of real data sets are studied; point and interval estimations of all parameters are obtained by maximum likelihood and bootstrap (parametric and non-parametric) procedures. Finally, the superiority of the proposed model over some common statistical distributions is shown through the different criteria for model selection including loglikelihood values, Akaike information criterion and Kolmogorov-Smirnov test statistic values.

Bi-s∗-concave distributions

Abstract We introduce new shape-constrained classes of distribution functions on R , the bi- s ∗ -concave classes. In parallel to results of Dumbgen et al. (2017) for what they called the class of bi-log-concave distribution functions, we show that every s -concave density f has a bi- s ∗ -concave distribution function F for s ∗ ≤ s ∕ ( s + 1 ) . Confidence bands building on existing nonparametric confidence bands, but accounting for the shape constraint of bi- s ∗ -concavity, are also considered. The new bands extend those developed by Dumbgen et al. (2017) for the constraint of bi-log-concavity. We also make connections between bi- s ∗ -concavity and finiteness of the Csorgő - Revesz constant of F which plays an important role in the theory of quantile processes.

Lp quantile regression with realized measure

Lp quantile regression with realized measure

Effect of education on obesity: Quantile regression approach

Effect of education on obesity: Quantile regression approach

Effects of Internet addiction on depression symptoms among adolescences in Jilin Province

Objective To explore the relationship between Internet addiction and depressive symptoms among adolescents, and to provide a scientific basis for preventing depression symptoms. Methods This study was conducted using a stratified random cluster sample method to select 2 149 junior school students reported Internet addiction and depressive symploms in Jilin Province. Propensity score method and quantile regression model were used to evaluate the association between Internet addiction and depressive symploms. Results Among the 2 149 participants, 386 (18.0%) were found Internet addictive. Prevalence of Internet addition differed significantly among students with different gender, grade, whether smoking or drinking, whether have corporal punishment from parents, sleep duration and body mass index ( P <0.05). Depressive symptom scores differed in students with smoking, corporal punishment from parents and insufficient sleep duration ( P <0.05). Quantile regression model found that with the quantile increasing, the risk of Internet addiction was also higher due to depressive symptoms ( P <0.05) . If Internet addiction occurred, their depressive symptom score would be improved 5.08 points for non-Internet addiction students. Conclusion Internet addiction shows potential impact on depression symptom students. The effect of Internet addiction increased with depression symptoms severity. The results of present study will provide a scientific basis for improving students’ mental health in the future. 【摘要】 目的 探讨青少年网络成瘾与抑郁症状的关系, 为预防学生抑郁提供科学依据。 方法 采用分层随机整群抽样 方法, 抽取吉林省 2 149 名初中生, 利用网络成瘾量表与抑郁量表进行问卷调査, 使用倾向得分匹配法和分位数回归分析 探讨网络成瘾和抑郁症状的内在关系。 结果 2 149 名被试中, 网络成瘾者 386 名, 检出率为 18.0%。不同性别、年级、是 否吸烟和是否饮酒、家长是否经常打骂、睡眠时间和体质量指数学生网络成瘾检出率差异均有统计学意义 ( P 值均<0.05)。 是否吸烟、家长打骂及不同睡眠时间学生抑郁症状总分比较, 差异均有统计学意义 ( P 值均<0.05)。百分位数回归模型发 现, 随着抑郁症状得分百分位水平的升髙, 网络成瘾影响抑郁症状的风险更髙 ( P <0.05)。在网络成瘾对抑郁症状的影响 效应中发现, 如果当前非网络成瘾学生出现网络成瘾, 抑郁症状总分会提髙 5.08 分。 结论 网络成瘾可以显著影响抑郁 症状, 并且随着抑郁症状严重程度的增加, 网络成瘾的作用也不断增强, 可为今后改善学生心理健康提供科学依据。

Type II Exponentiated Half Logistic Generated Family of Distributions with Applications

A new family of distributions called type II exponentiated half logistic is introduced and studied. Four new special models are presented. Some mathematical properties of the new family are studied. Explicit expressions for the moments, probability weighted moments, quantile function, mean deviation, order statistics and Renyi entropy are investigated. Parameter estimation of the family are obtained based on maximum likelihood procedure. Two real data sets are employed to show the usefulness of the new family.

A Machine Learning Based Approach to Predict Ostreopsis Cf. Ovata Bloom Events from Meteo- Marine Forecasts

Ostreopsis cf. ovata, a benthic toxic marine dinoflagellate, has been recorded along Italian coasts since the '90, but large bloom events have been reported only in recent years. In 2005, a monitoring programme started along the Ligurian coast (North-western Mediterranean), where time series of cell abundances have been collected for several sites, together with a range of related environmental variables. Data of cell abundances in 15 sites, together with environmental data provided by meteo-marine forecasting models used by the Regional Environmental Agency (ARPAL), have been used to implement a predictive modelling tool, able to forecast Ostreopsis cells concentration threshold exceedance as a function of meteo-marine forecasts. Starting from the experience of the predictive model implemented in 2015, the Quantile Regression Forest (QRF) has been applied: the model has been trained on past data (from 2015 until 2017) and tested with data taken during the two last available years (2018 and 2019). The use of this extremely adaptable regression model to evaluate threshold exceedance has shown a good capacity to predict overcoming events at a given spatial location. This tool can help the Regional Agency in the decision making process, providing an alert when/where a given alarm threshold is exceeded in order to trigger the emergency procedures. This is a first step in defining a predictive sampling strategy able to better capture bloom events.Keywords: classification, regression, machine learning, environmental data, Ostreopsis

A new look at the tourism development and economic growth nexus: International evidence:

This article tests the relationship between tourism development and economic growth in major tourism destinations in the world. The study relies on the quantile autoregressive distributed lag model...

Bias correction capabilities of quantile mapping methods for rainfall and temperature variables

Abstract This study aims to conduct a thorough investigation to compare the abilities of quantile mapping (QM) techniques as a bias correction method for the raw outputs from general circulation model (GCM)/regional climate model (RCM) combinations. The Karkheh River basin in Iran was selected as a case study, due to its diverse topographic features, to test the performances of the bias correction methods under different conditions. The outputs of two GCM/RCM combinations (ICHEC and NOAA-ESM) were acquired from the coordinated regional climate downscaling experiment (CORDEX) dataset for this study. The results indicated that the performances of the QMs varied, depending on the transformation functions, parameter sets, and topographic conditions. In some cases, the QMs' adjustments even made the GCM/RCM combinations' raw outputs worse. The result of this study suggested that apart from DIST, PTF:scale, and SSPLIN, the rest of the considered QM methods can provide relatively improved results for both rainfall and temperature variables. It should be noted that, according to the results obtained from the diverse topographic conditions of the sub-basins, the empirical quantiles (QUANT) and robust empirical quantiles (RQUANT) methods proved to be excellent options to correct the bias of rainfall data, while all bias correction methods, with the notable exceptions of performed PTF:scale and SSPLIN, performed relatively well for the temperature variable.

Tamanho da Turma e Desempenho Acadêmico dos Universitários: evidência para a UFPB

Resumo O objetivo do presente estudo consiste em investigar a relação entre o tamanho da classe e o desempenho acadêmico de estudantes do ensino superior. Para isso, utilizaram-se dados administrativos da Universidade Federal da Paraíba (UFPB), os quais contêm informações acadêmicas e socioeconômicas, como as notas dos alunos e o background familiar. Como estratégia empírica, estimou-se um modelo de dados em painel, com a inclusão de efeitos fixos e dummies para combinações de professor-semestre e disciplina-semestre, como forma de captar a influência de fatores não observados fixos e variantes do tempo, tendo em vista a endogeneidade do tamanho da turma. Empregou-se o método de Guimarães e Portugal (2009) para as estimativas na média e regressão quantílica para dados em painel presente em Koenker (2004). O coeficiente do tamanho da turma estimado pela média se mostrou negativo, embora sua magnitude seja bastante pequena, ao passo que as estimações por regressões quantílicas mostram um efeito negativo para os alunos com pior desempenho e um efeito positivo aos estudantes com as melhores notas.

Performance of Tropical Cyclone Forecast in Western North Pacific in 2015

ABSTRACT The operational track and intensity forecast errors of tropical cyclones (TCs) over the western North Pacific in 2015 were evaluated on the basis of RSMC-Tokyo’s “best-track” dataset. The results showed that position errors for each official agency were under 80 km, 130 km, 180 km, 260 km and 370 km at 24, 48, 72, 96 and 120 hr lead time. Stepped decreases in the values of each quantile were made at every lead times and have been made by global models from 2010 to 2015, especially for long lead time. The results of the Track Forecast Integral Deviation (TFID) show a clearly decreasing trend for most global models, indicating that the TC forecast tracks became increasingly similar to the observations. In 2015, the intensity forecast skill scores for both global and regional models were almost negative. However, the skill of EPSs’ intensity forecasting has made significant progress in the past year.

Estimation and evaluation on the return period of flash flood for small mountainous watersheds in the Han River basin

The objectives of this study are to estimate the return period of flash flood and evaluate its appropriateness based on the actual observation events for small mountainous watersheds in the Han River basin. For these goals, Flash Flood Guidance (FFG) was estimated from 1-hr duration Threshold Runoff (TR) and Saturation Deficit (SD) of soil moisture which was derived from Sejong University Rainfall Runoff (SURR) model. Then, the return period of flash flood was calculated by comparing the rainfall quantile to the 1-hr duration rainfall that exceeded the FFG during the past period (2002-2010). Moreover, the appropriateness of the estimated return period of flash flood was evaluated by using the observation events from 2011 to 2016. The results of the return period of flash flood ranged from 1.1 to 19.9 years with a mean and a standard deviation of 1.6 and 1.1 years, respectively. Also, the result of the appropriateness indicated that 83% of the return periods derived from observation events were within the return period of flash flood range. Therefore, the estimated return period of flash flood could be considered as highly appropriate.

Sistemas Convectivos de Mesosescala Associados a Eventos Extremos de Precipitação Sobre o Semiárido do Nordeste do Brasil

Este trabalho apresenta um estudo das caracteristicas fisicas e morfologicas dos Sistemas Convectivos de Mesoescala (SCM) que ocorreram entre os anos de 2010 e 2011 sobre a regiao Semiarida do Nordeste do Brasil em situacoes de eventos de precipitacao fraca (EPF) e intensa (EPI). Para tanto, foram utilizadas imagens do satelite Geostationary Operational Environmental Satellite (GOES-12) no canal infravermelho, obtidas a cada 30 minutos, dados esses utilizados pelo Forecasting and Tracking the Evolution of Cloud Clusters (10,7 μm) para identificar, rastrear e determinar as caracteristicas dos SCM. Alem disso, foram utilizados dados de precipitacao diaria, fornecidos pela Agencia Nacional de Aguas (ANA) e pelo Instituto Nacional de Meteorologia (INMET) para identificacao dos eventos extremos, por meio da tecnica dos quantis. Os resultados mostraram que os EPI foram mais frequentes que EPF, principalmente no mes de marco. Ambos SCM relacionados a EPF e EPI, apresentaram, em sua maioria, formato do tipo irregular. As temperaturas medias do topo da nuvem foram semelhantes (-52oC), porem as temperaturas minimas do topo dos SCM tiveram maiores ocorrencias nos meses mais quentes, com valor de -79oC. Os SCM relacionados aos EPI apresentaram maiores tamanhos. A partir desses resultados evidencia-se que os SCM atuantes sobre o Semiarido do Nordeste do Brasil possuem desenvolvimento vertical intenso, possivelmente favorecido pela presenca de um ou mais sistemas atmosfericos atuando simultaneamente.