The GM (1, N) model is an important prediction model among gray models, but the conventional GM (1, N) model shows big errors sometimes mainly due to the structural inconsistency between the gray differential equation for parameter estimation and the whitening equation for prediction. The paper derives the modified model of conventional gray differential equation based on the whitening equation, and

In this article, we propose inverse Gaussian correlated frailty model based on logistic exponential as baseline distribution. The Bayesian approach of Markov Chain Monte Carlo (MCMC) technique was employed to estimate the parameters involved in the models. A simulation study was performed to compare the true values and estimated value of the parameters. Comparison of the proposed model with correlated

Methods for joint analysis of longitudinal measures and right-censored survival outcomes have received much attention in the literature. However, in clinic and epidemiology research, the event of interest is only examined at the inspection time, resulting in the interval-censored failure time data. In this article, we construct a joint model for the longitudinal outcomes and the failure time, which

This article introduces a Bayesian multiple change point model for a collection of degradation signals in order to predict remaining useful life of rotational bearings. The model is designed for longitudinal data, where each trajectory is a time series segmented into multiple states of degradation using a product partition structure. An efficient Markov chain Monte Carlo algorithm is designed to implement

Due to the diversity of mobile network technologies, the volume of data that has to be observed by mobile operators in a daily basis has become enormous. This huge volume has become an obstacle to mobile networks management. This paper aims to provide a simplified representation of these data for an easier analysis. A model-based co-clustering algorithm for mixed data, functional and binary, is therefore

Latent class model (LCM), which is a finite mixture of different categorical distributions, is one of the most widely used models in statistics and machine learning fields. Because of its noncontinuous nature and flexibility in shape, researchers in areas such as marketing and social sciences also frequently use LCM to gain insights from their data. One likelihood-based method, the expectation–maximization

For a linear model on massive data, we propose an aggregated estimator depending on adaptive LASSO estimators. The proposed method allows the reduction of the data storage volume and the introduction of an aggregates estimator which automatically selects, with a probability converging to one, the significant explanatory variables. Moreover, the aggregated estimator, corresponding to the non null true

In this paper, we consider principal component analysis for α-stable random vectors. First, we present a new measure of dependence for bivariate α-stable vectors. The introduced measure is distribution based, symmetric, and linear in its arguments, and it measures the dispersion of an α-stable random variable. Then, using the proposed measure, we define principal components for α-stable vectors such

One major goal in clinical applications of multi-state models is the estimation of transition probabilities. Estimators based on subsampling were recently introduced by de Uña-Álvarez and Meira-Machado to estimate these quantities, and their superiority with respect to the competing estimators has been proved in situations in which the Markov condition is violated. The idea behind the proposed estimators

Efficient and precise calculation of the probability of false alarms for the K-distributed sea clutter and noise is crucial in radar detection system. We propose efficient and optimal importance sampling algorithms, in which two importance sampling estimators are constructed for both the product form and the compound form of the underlying K distribution. Moreover, we prove the existence and uniqueness

Introduced to model heavy-tailed actuarial loss data, the composite Lognormal–Pareto distribution is piecewise built from the Lognormal and Pareto densities defined on mutually disjoint intervals. When estimating its parameters, the main challenge is the estimation of the unknown threshold where the density form changes. Motivated by the uncertainty of this parameter, in this article we consider two

In this article, the performances of different point predictors of future record data based on some informative records from the two-parameter exponential distribution are studied in the sense of Pitman’s measure of closeness. On the basis of one-sample and two-sample prediction problems, we apply the Pitman’s measure of closeness to comparing the best unbiased, best invariant, maximum likelihood,

Stochastic blockmodels have been widely proposed as a probabilistic random graph model for the analysis of networks data as well as for detecting community structure in these networks. In a number of real-world networks, not all ties among nodes have the same weight. Ties among networks nodes are often associated with weights that differentiate them in terms of their strength, intensity, or capacity

Pitman measure of closeness (PMC) is a criterion to show how much an estimator is close to its parameter with respect to another estimator. We develop a new similarity measure which uses PMC on the basis of the similarity measure for hierarchical clustering algorithm that has better performance for heavy-tailed data.

This paper introduces policy incentive factors to explore their impact on innovation diffusion in enterprise clusters, and reveals the relationship between policy incentives and innovation adoption ratio through dynamic correlation simulation of policy incentive intensity and the type and scale of enterprise clusters. The research results show that policy incentives have short-term timeliness, and

This paper considers the estimation for a partial index additive regression model, when the response variable and covariates in the index part are observed with additive distortion measurement errors. For the index parameter, the dimension-reduction based estimators with or without additive distortion measurement errors are proposed. This new estimation method is further adopted to the partial linear

This paper provides an objective Bayesian inference method with partial information based on generalized Type-II progressive hybrid censored data. We first derive a reference prior with partial information on the unknown parameters and then show that this leads to suitable posterior distributions. In addition, we provide a prediction method based on the derived reference prior with partial information

The objective of this paper is to provide a new algorithm for the regression of survival data. We propose an algorithm of Survival twin support vector regression (STWSVR), an extension of Twin support vector regression (TWSVR) in binary classification, to explore the analysis of survival data with right censoring. The main algorithm STWSVR is to solve a pair of quadratic programing problems (QPPs)

Most studies of estimating overlapping coefficients assume two specific parametric models for population densities. The methods that used such of this assumption are called parametric methods, which work well when these model assumptions are valid. Violation of parametric model assumption often leads to a poor estimation. The alternative nonparametric methods are more flexible than the parametric one

The traditional network reliability research generally focuses on the topological connectivity among given nodes based on graph theory. Without considering the relationship between network failures and network performance degradation, traditional network reliability analysis cannot reflect actual network ability reaching up to a certain accomplishment level over a utilization interval. As for the performability

In order to show the effect of treatment, the change between two repeated psychometric measurements at the individual level should be estimated. The simplest method is to calculate the absolute difference between two measurements. However, measurements obtained in a clinical setting are often influenced by other changes not related to the treatment. One of the typical sources of error is regression

A general linear random-effects model y=Xβ+ε with β=Zα+γ that includes both fixed and random effects and its two transformed models A: Ay=AXZα+AXγ+Aε and B: By=BXZα+BXγ+Bε are considered without making any restrictions on correlation of random effects and any full rank assumptions. Predictors of joint unknown parameter vectors under the transformed models A and B have different algebraic expressions

A control chart is very useful to control assignable causes that detect the shifted process parameters (e.g., mean and dispersion). Simultaneous monitoring of the process parameters is a well-known approach utilized for the bilateral processes. This paper explores the joint monitoring to study the impact of both process mean and coefficient of variation (CV) by using log-normal transformation under

In many industrial and non-industrial processes, the quality of a product or a process are described by a functional relationship between a response variable and one or more independent variables, known as profile. On the other hand, the process capability indices provide numerical measures of the process ability. Few researches have been done to evaluate the process capability of profiles; especially

In this paper, we establish the consistency and the asymptotic normality of the Periodic Poisson (respectively the Periodic Geometric) Quasi Maximum Likelihood estimators, (P−PQML) (respectively (P−GQML), of a general class of periodic count time series models. In this class, the conditional mean is expressed as a parametric and measurable function, with periodic parameters, of the infinite past of

In this article, we propose a finite buffer impatient customer queue with second optional service (SOS) and working vacations. When the server is busy, an arriving customer either joins the queue or balks on the basis of state-dependent joining/balking probabilities. For each customer, the server provides two phases of service, namely, first essential service (FES) and SOS. All the customers demand

Multiple change-points problem has been discussed recently on the background of financial market. As a financial crisis or big event happened, the government should increase the macro-control ability in order to mitigate property damage. The above issue can be resolved through finding a more accurate model which fits the peculiar financial asset price, and finding a more efficient test. This paper

We consider a stochastic frontier regression model with a time dependent efficiency process, which is assumed to follow an exponential autoregressive sequence. The likelihood for the model is derived in the context of a bivariate exponential distribution. Bayesian method is suggested for the estimation of parameters. We apply the model and the estimation procedure to a panel of US airlines data and

The large literature on software reliability assessment and prediction is essentially concerned with parametric models. During the last decade non-parametric models have been developed. This paper presents a new non-parametric model which is based on the idea of separation of concern between the long term trend in reliability growth and the local behaviour The trend is captured using moving averages

In this article, some new results and inequalities of dynamic failure entropy (DFE) defined by the author Abbasnejad in 2011 will be obtained. Then weighted versions of failure and survival entropies of order β introduced and some properties and inequalities based on these uncertainty measures found. Then weighted DFE will be introduced and some properties investigated. Finally, empirical cumulative

It is well-known that optimal designs for logistics regression models depend on the unknown parameter values. In practice, guess values are used as proxies. Thus the actual design implemented is a pseudo optimal design. In this article, we assess whether this problem in optimality from ill-guessed parameter values can be improved by a 2-stage optimal design. We examine the optimal allocation of resources

In this paper, we define the adaptive Weighted Nadaraya–Watson estimation (AWNW) of the conditional distribution function (cdf) for independent and identically distributed (iid) data using varying bandwidth. The asymptotic normality of the proposed estimator is investigated. The results of the simulation studies show that the proposed estimation have better performance than the Weighted Nadaraya–Watson

For the linear combinator of the slope vector in the multiple measurement error model, existing confidence interval is so seriously affected by Gleser–Hwang effect that it is subject to have poor empirical coverage and unacceptable length. Moreover, it is frequent absence when the data is unbelievable. This article therefore construct a new confidence interval which is always available and slightly

The joint censoring scheme is of great importance when the motive of study is to compare the relative merits of products in relation of their service times. In last few years, progressive censoring received considerable attention in order to save cost and time of the experiment. This paper deals with inferences for Lindley populations, when joint progressive type-II censoring scheme is applied on two

In this paper, we use the likelihood ratio test to detect changes in the parameters of the skew slash distribution. Simulations have been conducted under different scenarios to investigate the performance of the proposed method. In the end, the real data applications are studied.

The modified ridge-type estimator has been shown to cushion the effects of multicollinearity in the linear regression model. Recent studies have shown the adverse effects of multicollinearity in the gamma regression model (GRM). We proposed a gamma modified ridge-type estimator to tackle this problem. We derived the properties of this estimator and conducted a theoretical comparison with some of the

In this article we derive the exact null distribution of a class of Hollander-Proschan type statistics for testing exponentiality against NWBUE alternatives. The exact null distribution of a previously established statistic is obtained as a special case. Critical values are tabulated for different combinations of parameter values. Performance of the tests for small sample sizes has also been studied

The single-index models (SIMs) provide an efficient way of coping with high-dimensional nonparametric estimation problems and avoid the “curse of dimensionality.” Many existing estimation procedures for SIMs were built on least square loss, which is popular for its mathematical beauty but is non-robust to non-normal errors and outliers. This article addressed the question of both robustness and efficiency

In this paper, we propose a k-th correlation coefficient estimator between the density function and distribution function of the model errors in single-index models and partial linear single-models. This k-th correlation coefficient estimator is used to test whether the density function of the true model error is symmetric or not. First, we propose a moment based estimator of k-th correlation coefficient

In the survey sampling, when the study and auxiliary variables are tolerably correlated, the ranks of an auxiliary variable also relate mutually with the study variable and thus by using these ranks as a valid mechanism, increases the accuracy of an estimator. In the current study, an enhanced estimator of the finite population mean is proposed that uses the ancillary information in form of the ranks

This article deals with some probabilistic and statistical properties of a periodic exponential GARCH(1,1) model, which is very adequate and appropriate to capture and describe, at the same time, three stylized facts very often encountered in the field of financial time series namely, the volatility clustering, the asymmetry and the periodicity exhibited by the autocovariance structure. Indeed, necessary

Background: In comparative research on time-to-event data for two groups, when two survival curves cross each other, it may be difficult to use the log-rank test and hazard ratio (HR) to properly assess the treatment benefit. Our aim was to identify a method for evaluating the treatment benefits for two groups in the above situation. Methods: We quantified treatment benefits based on an intuitive measure

Selecting the number of slice is a key step for the implement of the sliced average variance estimation (SAVE) method. To our knowledge, there is no widely accepted method for it in a practical application. And an incorrect number of the slice may leads to an inaccurate conclusion. In traditional multivariate sufficient dimension reduction procedure, it is usually to adopt the fuze approach which combined

L1 regularized logistic regression consists an important tool in data science and is dedicated to solve sparse generalized linear problems. The L1 regularization is widely used in variable selection and estimation in generalized linear model analysis. This approach is intended to select the statistically important predictors. In this paper we compare the performance of some existing L1 regularized

Actuaries are often in search of new distributions suitable for modeling financial and insurance losses. In this work, we propose a new family of distributions, called a new beta power transformed family of distributions. A special sub-model of the proposed class, called a new beta power transformed Weibull, suitable for modeling heavy tailed data in the scenario of actuarial statistics and finance

In the sampling approaches framework, the combination of the information on the sizes of the nonsampled units can help to attain better estimators by using semiparametric models. Sometimes, the design variables that have an important role in the sampling mechanism are not available. Hence predictions require to be adapted for the consequence of selection. To infer the population mean in a sample survey

Seemingly unrelated regression (SUR) models were applied when several linear regression equations were investigated at the same time. To reduce the multicollinearity influence in the SUR models, the one-parameter ridge (Ridge-1) solution was proposed and discussed by some researchers. As a generalization of the Ridge-1 solution, in the context of SUR models having multicollinearity problem, the two-parameter

For long-time running electric devices used in satellites, the accurate reliability prediction is crucial in engineering. The reliability of these devices is often directly related to the degradation of a performance characteristic. However, the problem about predicting the reliability of these devices based on a subset which is chosen from the real-time data flow adaptively has received scant attention

Capability analysis allows evaluating the conformity of the production to the project specifications in industrial processes. Different indices can be used to assess the process capability, among them the Cpm (or Taguchi) index. In this work we propose the estimation of Cpm for normally distributed processes using ranked set sampling (RSS) and two extensions: pair ranked set sampling (PRSS), as an

In this article, we propose a parent-generalized family of chain exponential ratio type estimators in stratified random sampling to estimate the finite population mean using known information on two supplementary variables. The proposed family covered all the well-known family of existing ratio, product, chain ratio, chain product, chain exponential ratio and chain exponential product type estimators

Abstract In this paper, we mainly study the asymptotic normality of wavelet estimators in the heteroscedastic regression model with φ-mixing errors. Under some suitable conditions, the asymptotic normality of the wavelet estimators of g and f in the heteroscedastic regression model with φ-mixing errors are obtained, which generalize or improve the corresponding ones for α-mixing random variables and

Recent technological developments have enabled us to collect complex and high-dimensional data in many scientific fields, such as population health, meteorology, econometrics, geology, and psychology. It is common to encounter such datasets collected repeatedly over a continuum. Functional data, whose sample elements are functions in the graphical forms of curves, images, and shapes, characterize these

Uncertain time series models have been investigated to predict future values based on imprecise observations. The existing researches focus on how to estimate unknown parameters in the uncertain time series model without considering how to determine the lag order. This paper proposes three types of cross validation methods, i.e. fixed origin cross validation, rolling origin cross validation, and rolling

Recent research in the area of univariate mixture modeling indicated that the finite mixture models based on Burr and Inverse Burr component distributions perform well in the modeling of heavy-tail insurance data. Mixture models are able to capture the multimodality which is quite a common characteristic of insurance losses. Through an extensive simulation study, we assess the performance of three

Consider the nonparametric curve estimation problem. Wavelet shrinkage methods are applied to the data in the wavelet domain for noise reduction. After denoising, the function can be estimated by wavelet basis expansion. The present paper proposes a Bayesian approach for wavelet shrinkage with the use of a mixture of a point mass function at zero and the logistic distribution symmetric around zero

This paper develops a Bayesian nonparametric model for skewed spatial data with nonstationary dependence structure. A transformed Gaussian model is proposed for the atoms of the kernel stick-breaking process by transforming the margins of a Gaussian process to flexible marginal distributions. This study proves that the correlation structure of the underlying spatial process is nonstationary. Results

This paper deals with a new two-parameter lifetime distribution with increasing, decreasing and constant hazard rate. This distribution allows the occurrence of zero values and involves the exponential, linear exponential and other combinations of Weibull distributions as submodels. Many statistical properties of the distribution are derived. Maximum likelihood estimation of the parameters and a bias

In this article, we consider estimating the parameters, reliability function R(t) and failure rate function H(t) of the two-parameter bathtub-shaped distribution introduced by Chen (2000 Chen, Z. 2000. A new two-parameter lifetime distribution with bathtub shape or increasing failure rate function. Statistics & Probability Letters 49 (2):155–61.[Crossref], [Web of Science ®] , [Google Scholar]) based

This paper is concerned with providing some computation-related details of the 16R packages that have been developed by Demirtas and his colleagues in the context of random number generation. The dominant theme is multivariate mixed data generation. However, univariate and multivariate data generation from different distributions as well as some other tools such as modeling the correlation transitions

In any clinical case, a decision is made with the maximum possible accuracy. To achieve such accuracy, in the presence of multiple diagnostic tests or biomarkers, biomarker combinations aim to achieve maximum accuracy. As existing biomarker combination methods combine only continuous biomarkers, therefore in this study biomarker combination for binary biomarkers was created by suggesting an approach