A KERNEL-TYPE ESTIMATOR OF A QUANTILE FUNCTION UNDER RANDOMLY TRUNCATED DATA

A kernel-type estimator of the quantile function Q（p） = inf{t：F（t） ≥ p}, 0 ≤ p ≤ 1, is proposed based on the kernel smoother when the data are subjected to random truncation. The Bahadur-type representations of the kernel smooth estimator are established, and from Bahadur representations the authors can show that this estimator is strongly consistent, asymptotically normal, and weakly convergent.

Applications of Burr Ⅲ-Weibull quantile function in reliability analysis

This paper introduces a new family of distributions defined in terms of quantile function.The quantile function introduced here is the sum of quantile functions of life time distributions called Burr Ⅲ and Weibull.Different distributional characteristics and reliability properties are included in the study.Method of Least Square and Method of L-moments are applied to estimate the parameters of the model.Two real life data sets are used to illustrate the performance of the model.

Scatter factor confidence interval estimate of least square maximum entropy quantile function for small samples

Classic maximum entropy quantile function method （CMEQFM） based on the probability weighted moments （PWMs） can accurately estimate the quantile function of random variable on small samples, but inaccurately on the very small samples. To overcome this weakness, least square maximum entropy quantile function method （LSMEQFM） and that with constraint condition （LSMEQFMCC） are proposed. To improve the confidence level of quantile function estimation, scatter factor method is combined with maximum entropy method to estimate the confidence interval of quantile function. From the comparisons of these methods about two common probability distributions and one engineering application, it is showed that CMEQFM can estimate the quantile function accurately on the small samples but inaccurately on the very small samples （10 samples）; LSMEQFM and LSMEQFMCC can be successfully applied to the very small samples; with consideration of the constraint condition on quantile function, LSMEQFMCC is more stable and computationally accurate than LSMEQFM; scatter factor confidence interval estimation method based on LSMEQFM or LSMEQFMCC has good estimation accuracy on the confidence interval of quantile function, and that based on LSMEQFMCC is the most stable and accurate method on the very small samples （10 samples）.

Quantile Version of Mathai-Haubold Entropy of Order Statistics

Many researchers measure the uncertainty of a random variable using quantile-based entropy techniques.These techniques are useful in engineering applications and have some exceptional characteristics than their distribution function method.Considering order statistics,the key focus of this article is to propose new quantile-based Mathai-Haubold entropy and investigate its characteristics.The divergence measure of theMathai-Haubold is also considered and some of its properties are established.Further,based on order statistics,we propose the residual entropy of the quantile-based Mathai-Haubold and some of its property results are proved.The performance of the proposed quantile-based Mathai-Haubold entropy is investigated by simulation studies.Finally,a real data application is used to compare our proposed quantile-based entropy to the existing quantile entropies.The results reveal the outperformance of our proposed entropy to the other entropies.

Bias Correction Technique for Estimating Quantiles of Finite Populations under Simple Random Sampling without Replacement

In this paper, the problem of nonparametric estimation of finite population quantile function using multiplicative bias correction technique is considered. A robust estimator of the finite population quantile function based on multiplicative bias correction is derived with the aid of a super population model. Most studies have concentrated on kernel smoothers in the estimation of regression functions. This technique has also been applied to various methods of non-parametric estimation of the finite population quantile already under review. A major problem with the use of nonparametric kernel-based regression over a finite interval, such as the estimation of finite population quantities, is bias at boundary points. By correcting the boundary problems associated with previous model-based estimators, the multiplicative bias corrected estimator produced better results in estimating the finite population quantile function. Furthermore, the asymptotic behavior of the proposed estimators is presented. It is observed that the estimator is asymptotically unbiased and statistically consistent when certain conditions are satisfied. The simulation results show that the suggested estimator is quite well in terms of relative bias, mean squared error, and relative root mean error. As a result, the multiplicative bias corrected estimator is strongly suggested for survey sampling estimation of the finite population quantile function.

The Doubly Truncated Generalized Log-Lindley Distribution

The aim of this paper is to present generalized log-Lindely (GLL) distribution, as a new model, and find doubly truncated generalized log-Lindely (DTGLL) distribution, truncation in probability distributions may occur in many studies such as life testing, and reliability. We illustrate the applicability of GLL and DTGLL distributions by Real data application. The GLL distribution can handle the risk rate functions in the form of panich and increase. This property makes GLL useful in survival analysis. Various statistical and reliability measures are obtained for the model, including hazard rate function, moments, moment generating function, mean and variance, quantiles function, Skewness and kurtosis, mean deviations, mean inactivity time and strong mean inactivity time. The estimation of model parameters is justified by the maximum Likelihood method. An application to real data shows that DTGLL distribution can provide better suitability than GLL and some other known distributions.

The Modi Exponentiated Exponential Distribution

In this study, a new four-parameter distribution called the Modi Exponentiated Exponential distribution was proposed and studied. The new distribution has three shape and one scale parameters. Its mathematical and statistical properties were investigated. The parameters of the new model were estimated using the method of Maximum Likelihood Estimation. Monte Carlo simulation was used to evaluate the performance of the MLEs through average bias and RMSE. The flexibility and goodness-of-fit of the proposed distribution were demonstrated by applying it to two real data sets and comparing it with some existing distributions.

Vibration Fatigue Probabilistic Life Prediction Model and Method for Blade

Vibration fatigue is one of the main failure modes of blade.The vibration fatigue life of blade is scattered caused by manufacture error,material property dispersion and external excitation randomness.A new vibration fatigue probabilistic life prediction model(VFPLPM)and a prediction method are proposed in this paper.Firstly,as one-dimensional volumetric method(ODVM)only considers the principle calculation direction,a three-dimensional space vector volumetric method(TSVVM)is proposed to improve fatigue life prediction accuracy for actual threedimensional engineering structure.Secondly,based on the two volumetric methods(ODVM and TSVVM),the material C-P-S-N fatigue curve model(CFCM)and the maximum entropy quantile function model(MEQFM),VFPLPM is established to predict the vibration fatigue probabilistic life of blade.The VFPLPM is combined with maximum stress method(MSM),ODVM and TSVVM to estimate vibration fatigue probabilistic life of blade simulator by finite element simulation,and is verified by vibration fatigue test.The results show that all of the three methods can predict the vibration fatigue probabilistic life of blade simulator well.VFPLPM &TSVVM method has the highest computational accuracy for considering stress gradient effect not only in the principle calculation direction but also in other space vector directions.

Kumaraswamy Inverted Topp–Leone Distribution with Applications to COVID-19 Data

In this paper, an attempt is made to discover the distributionof COVID-19 spread in different countries such as;Saudi Arabia, Italy,Argentina and Angola by specifying an optimal statistical distribution for analyzing the mortality rate of COVID-19. A new generalization of the recentlyinverted Topp Leone distribution, called Kumaraswamy inverted Topp–Leonedistribution, is proposed by combining the Kumaraswamy-G family and theinverted Topp–Leone distribution. We initially provide a linear representationof its density function. We give some of its structure properties, such as quantile function, median, moments, incomplete moments, Lorenz and Bonferronicurves, entropies measures and stress-strength reliability. Then, Bayesian andmaximum likelihood estimators for parameters of the Kumaraswamy invertedTopp–Leone distribution under Type-II censored sample are considered.Bayesian estimator is regarded using symmetric and asymmetric loss functions. As analytical solution is too hard, behaviours of estimates have beendone viz Monte Carlo simulation study and some reasonable comparisonshave been presented. The outcomes of the simulation study confirmed theefficiencies of obtained estimates as well as yielded the superiority of Bayesianestimate under adequate priors compared to the maximum likelihood estimate.Application to COVID-19 in some countries showed that the new distributionis more appropriate than some other competitive models.

Gumbel-Exponentiated Weibull {Logistic} Lifetime Distribution and Its Applications

A new generalized exponentiated Weibull model called Gumbel-exponentiated Weibull {Logistic} distribution is introduced and studied. The new distribution extends the exponentiated Weibull distribution with additional parameters and bimodal densities. Some new and earlier distributions formed the sub-models of the proposed distribution. The mathematical properties of the new distribution including expressions for the hazard function, survival function, moments, order statistics, mean deviation and absolute mean deviation from the mean, and entropy were derived. Monte Carlo simulation study was carried out to assess the finite sample behavior of the parameter estimates by maximum likelihood estimation approach. The superiority of the new generalized exponentiated Weibull distribution over some competing distributions was proved empirically using the fitted results from three real life datasets.

Quantile-Based Shannon Entropy for Record Statistics

The quantile-based entropy measures possess some unique properties than their distribution function approach.The present communication deals with the study of the quantile-based Shannon entropy for record statistics.In this regard a generalized model is considered for which cumulative distribution function or probability density function does not exist and various examples are provided for illustration purpose.Further we consider the dynamic versions of the proposed entropy measure for record statistics and also give a characterization result for that.At the end,we study F^(α)-family of distributions for the proposed entropy measure.

Statistical Inference of Truncated Weibull-Rayleigh Distribution: Its Properties and Applications

In this paper, we derived a new distribution named as truncated Weibull Rayleigh (TW-R) distribution. Its characterization and statistical properties are obtained, such as reliability function, hazard function, reversed hazard rate function, cumulative hazard rate function, quantile function, rth moment, incomplete moments, Rényi and q entropies and order statistic. Parameter estimation is implemented using method of maximum-likelihood estimation and Fisher information matrix is derived. Finally, application of the presented new distribution to a real data representing the failure times of 63 airbcraft Windshield is given and its goodness-of-fit is demonstrated. In addition to, comparisons to other models are implemented to show the flexibility of the presented model.

Partial functional linear quantile regression

This paper studies estimation in partial functional linear quantile regression in which the dependent variable is related to both a vector of finite length and a function-valued random variable as predictor variables. The slope function is estimated by the functional principal component basis. The asymptotic distribution of the estimator of the vector of slope parameters is derived and the global convergence rate of the quantile estimator of unknown slope function is established under suitable norm. It is showed that this rate is optirnal in a minimax sense under some smoothness assumptions on the covariance kernel of the covariate and the slope function. The convergence rate of the mean squared prediction error for the proposed estimators is also established. Finite sample properties of our procedures are studied through Monte Carlo simulations. A real data example about Berkeley growth data is used to illustrate our proposed methodology.

Some results on quantile-based Shannon doubly truncated entropy

Sunoj et al.[(2009).Characterization of life distributions using conditional expectations of doubly(Intervel)truncated random variables.Communications in Statistics–Theory and Methods,38(9),1441–1452]introduced the concept of Shannon doubly truncated entropy in the literature.Quantile functions are equivalent alternatives to distribution functions in modelling and analysis of statistical data.In this paper,we introduce quantile version of Shannon interval entropyfor doubly truncated random variable and investigate it for various types of univariate distribution functions.We have characterised certain specific lifetime distributions using the measureproposed.Also we discuss one fascinating practical example based on the quantile data analysis.

Weighted quantile regression for longitudinal data using empirical likelihood

This paper proposes a new weighted quantile regression model for longitudinal data with weights chosen by empirical likelihood(EL). This approach efficiently incorporates the information from the conditional quantile restrictions to account for within-subject correlations. The resulted estimate is computationally simple and has good performance under modest or high within-subject correlation. The efficiency gain is quantified theoretically and illustrated via simulation and a real data application.

Behavioral Portfolio Selection with Loss Control

In this paper we formulate a continuous-time behavioral （4 la cumulative prospect theory） portfolio selection model where the losses are constrained by a pre-specified upper bound. Economically the model is motivated by the previously proved fact that the losses Occurring in a bad state of the world can be catastrophic for an unconstrained model. Mathematically solving the model boils down to solving a concave Choquet minimization problem with an additional upper bound. We derive the optimal solution explicitly for such a loss control model. The optimal terminal wealth profile is in general characterized by three pieces： the agent has gains in the good states of the world, gets a moderate, endogenously constant loss in the intermediate states, and suffers the maximal loss （which is the given bound for losses） in the bad states. Examples are given to illustrate the general results.