Abstract The use of an appropriate design is a very important aspect of agroforestry experimentation. Since different species of trees may interact spatially, plots in agroforestry could be connected through network of trees which would create non-directional adjacency effects on a plot. Hence, there is a need to take the “direct effect of trees” and “tree network effects” on the crop into account

Abstract In this paper, we design the repetitive group sampling plan under Birnbaum–Saunders distribution, since this distribution is considered as an appropriate and flexible distribution to model different types of data particularly lifetime data. In this study, the Birnbaum–Saunders distributed mean lifetime of the product is considered as the quality characteristic where the shape parameter is

Abstract We consider modeling and analysis of functional data arising in method comparison studies. The observed data consist of repeated measurements of a continuous variable obtained using multiple methods of measurement on a sample of subjects. The data are treated as multivariate functional data that are observed with noise at a common set of discrete time points which may vary from subject to

Abstract Bayesian bandwidth selections in continuous associated kernel estimation of probability density function are very good alternatives to classical methods like cross-validation techniques. In this paper, we examined the behavior of Bayesian variable bandwidths in gamma kernel estimation, developed theoretically in Wansouwé et al. [Ake: An R Package for Discrete and Continuous Associated Kernel

Abstract Missingness or incompleteness of data is a challenge to the survey statisticians in producing the reliable estimates of the desired population parameters. If the missingness pattern is unidentifiable, this is the case of random non-response and to deal with such situations, this work proposes some alternative imputations methods to cope with the missing data. The proposed imputation methods

Abstract Bootstrap technique has an extensive use in almost all branches of statistics including regression analysis. The classical regression theory heavily depends on normality assumption of errors, which does not often hold in practice. Bootstrap technique can be readily applied to a situation when we do not assume any distribution for the error and that is why bootstrapping in regression is becoming

Abstract In real life situations, for example, in case of concrete structures and mechanical devises, failure occurs due to accumulation of stress over time, and the consequent degradation causes the system to under-perform, thereby failure occurs completely, when the accumulated stress exceeds a critical threshold. The degradation path is known for some systems and in such cases useful information

Abstract This article presents error estimates for the expected value and variance of the stochastic process of transverse displacement of an Euler-Bernoulli beam, with uncertainty in the mechanical properties of the materials. Estimators are obtained using the Neumann-Monte Carlo, Yamazaki et al. (1988 Yamazaki, F. , M.Shinozuka, and G.Dasgupta . 1988. Neumann expansion for stochastic finite element

Abstract In this paper, a risk model driven by spectrally negative Lévy processes is considered where dividends until a general draw-down time are paid under a constant barrier strategy. In this model, the moments of the sum of the discounted dividend payments until the general draw-down time are derived through the corresponding scale functions. The explicit expressions for the Laplace transform of

Abstract Multicollinearity, a common problem encountered in regression analysis, has many adverse effects on the ordinary least squares estimator. According to the literature, the ridge regression estimator is one of the useful remedies to overcome this problem. The present study is aimed to use the Bayesian approach for ridge regression and to use estimation of biasing parameters in the Bayesian paradigm

Abstract Mixed responses within clusters are becoming an increasingly popular phenomenon. However, this scenario is a complex one. This research is based on survival and count response data which occur frequently in medical studies. In the literature, there is no satisfactory goodness of fit test for a joint model of these two responses within a cluster setting. Therefore, the main objective of this

Abstract In this article, we introduce two parameter estimator and two parameter weighted mixed estimator for the vector of parameters in linear measurement error models. The asymptotic properties of the estimators are derived and the performance of obtained estimators is examined with respect to the mean square error matrix criterion. Furthermore, selection of the biasing parameters is discussed.

Abstract In this paper, we consider filtration-consistent nonlinear expectations which satisfy a general domination condition. We show that filtration-consistent nonlinear expectations can be represented by g-expectations defined by the solutions of backward stochastic differential equations, whose generators are independent of y and uniformly continuous in z. As an application, we establish a concentration

Abstract The efficient estimation problem of a semi-parametric first-order periodic integer-valued autoregressive (PINAR(1)) model is considered. The unspecified distribution of the innovation process of this model is suposed to satisfy only some mild technical assumptions. We therefore provide efficient estimates for both parameters of the model, namely a periodic autoregressive parameter and a periodic

Abstract Multivariate isotonic regression theory plays an important role in the field of testing statistical hypotheses under order restriction for several vector valued parameters and this problem may arise in several situations. Sasabuchi et al. derived a likelihood ratio type test for homogeneity of order restricted mean vectors under a multivariate normality assumption with unknown and common covariance

Abstract Statistical inference on the location of the optima (global maxima or minima) is one of the main goals in the area of Response Surface Methodology, with many applications in engineering and science. While there exist previous methods for computing confidence regions on the location of optima, these are for linear models based on a Normal distribution assumption, and do not address specifically

Abstract Data characteristics are often summarized and represented by a set of variables. Identifying the relationship between these variables is crucial for prediction, hypothesis testing, and decision making. The relation between two variables is often quantified using a correlation factor. Once the correlation between a response and an independent variable is quantified, it can be used to make predictions

Abstract The elementary effects method is a screening global sensitivity measure to identify the few important inputs in a model which contains many inputs. In this work, a new advanced trajectory design method (NAT method) is developed to calculate the global sensitivity indices based on the elementary effects method. The conception of trajectory cube is proposed to define a trajectory, and then the

Abstract Scale mixtures of normal distributions are useful for statistical procedures involving symmetric and heavy-tailed data. Ferreira, Lachos, and Bolfarine (2016 Ferreira, C. S. , V. H.Lachos, and H.Bolfarine . 2016. Likelihood-based inference for multivariate skew scale mixtures of normal distributions. AStA Advances in Statistical Analysis 100 (4):421–41. doi:10.1007/s10182-016-0266-z.[Crossref]

Abstract The aim of this paper is to consider the bivariate inverse generalized exponential distribution which has a singular component. The bivariate inverse generalized exponential distribution can be used when the marginals have heavy tailed distributions, and they have non-monotone hazard functions. Due to presence of the singular component, it can be used quite effectively when there are ties

In this article, we investigate the mathematical properties of the Lehmann type II inverse Weibull distribution. We show that this model is a reparameterized version of the Kumaraswamy-inverse Weibull distribution without identifiability problems. Parameter estimation is discussed using maximum likelihood (ML) method under a right-censoring scheme. Furthermore, a bootstrap resampling approach is considered

In recent environmental studies, the examination of extreme events has great impact. The block maxima of environment-related indices can be analyzed by the tools of extreme value theory. For instance, the monthly maxima of the fire weather index at stations in British Columbia might be modeled by GEV distribution, but it is questionable whether the underlying stochastic process is stationary. This

The issue of this paper is the inference about the parameters of modified extended exponential distribution (MExED) planned on the progressively type-II censored (Prog -II- C) samples. The maximum likelihood and Bayesian estimates are procured for the parameters of MExED. Approximate confidence intervals (ACIs), the reliability and risk functions are estimated based on the asymptotic distribution of

The weighted total least-squares (WTLS) estimate for the partial EIV (PEIV) model is very susceptible to outliers. In view of outliers in observations and coefficient matrix, a robust estimate of WTLS which makes using of iteratively reweighted technique (IRTLS) for the PEIV model is developed by combining a new two-step iterated algorithm of the WTLS estimate with the sensitivity-analysis based on

In survival analysis naturally observed lifetimes are not of large size and so the most commonly used maximum likelihood (ML) estimator that is often biased needs to be corrected in the sense of bias. In this paper, exact expression for the Fisher Information matrix under progressive type-I interval censoring (PTIC) scheme is given. Using the method proposed by Cox and Snell (J. Royal Stat. Soc. B

Joint modeling of longitudinal and survival data is becoming increasingly popular. It can properly handle multiple issues commonly encountered in longitudinal studies, such as endogenous time-dependent covariates and informative missingness. There are several statistical packages for conducting Bayesian joint modeling analysis, two of which are JMbayes, which has been applied in biomedical research

In this paper, we propose a new estimation methodology based on a projected non-linear conjugate gradient (PNCG) algorithm with an efficient line search technique. We develop a general PNCG algorithm for a survival model incorporating a proportion cure under a competing risks setup, where the initial number of competing risks are exposed to elimination after an initial treatment (known as destruction)

In this work, we interest to the estimation of the steady-state probabilities of the two processes (the hidden Markov chain and the emission process) composing a standard Hidden Markov Model. Since the estimation of steady-state Markov chain is well known, more attention will be given to the estimation of the steady-state of the emission process. Among different methods, we will focus more on the technique

In this paper, we study how to estimate the optimal dividend strategy in a discrete-time risk model with delayed claims. The insurance company pays dividends to shareholders, and each dividend is taxable. The company controls the dividend payments to maximize the expectation of accumulated discounted dividends after tax prior to ruin. This paper aims to develop a method to estimate the optimal dividend

In this article, we consider asymptotic properties of support vector machine (SVM) in high-dimension, low-sample-size (HDLSS) settings. In particular, we treat high-dimensional imbalanced data. We investigate behaviors of SVM for a regularization parameter C in a framework of kernel functions. We show that SVM cannot handle imbalanced classification, and SVM is very biased in HDLSS settings. In order

Quantile regression has become a standard modern econometric method because of its capability to investigate the relationship between economic variables at various quantiles. The econometric method of Markov-switching regression is also considered important because it can deal with structural models or time-varying parameter models flexibly. A combination of these two methods, known as “Markov-switching

In this article, we study the situation of observations whose ranks cannot be determined for the auxiliary variable in the Ranked Set Sampling (RSS) method. Therefore, we examine the case of tie information for ratio estimators of the population mean. We propose a new exponential ratio estimator using the modified isotonic estimator for this situation. Simulation results show that the proposed estimator

Lomax distribution has been widely used in economics, business and actuarial sciences. Due to its importance, we consider the statistical inference of this model under joint type-II censoring scenario. In order to estimate the parameters, we derive the Newton-Raphson(NR) procedure and we observe that most of the times in the simulation NR algorithm does not converge. Consequently, we make use of the

Statistical process control (SPC) is a method of monitoring, controlling and improving a process through statistical analysis. In real applications, many industrial process data may follow a positively-skewed distribution such as the lognormal distribution. In this article, under lognormal process, we study the performance of three X ¯ control charts, Shewhart control chart, weighted variance control

This article puts forward an idea of recognizing distributions rather than carrying-out classic goodness-of-fit tests (GoFTs). For the purpose of recognizing the k-nearest neighbors (kNN) rule is applied. We focus the reader’s attention on recognizing the normal distribution. The main part of the article is devoted to the computer implementation of a classifier of distributions that involves kNN rule

The reciprocal LASSO estimate for linear regression corresponds to a posterior mode when independent inverse Laplace priors are assigned on the regression coefficients. This paper studies reciprocal LASSO in quantile regression from a Bayesian perspective. Simple and efficient Gibbs sampling algorithms are developed for posterior inference using a scale mixture of inverse uniforms (or double Pareto

This article proposes another general class of biased estimators which includes some popular estimators as special cases and discusses its properties for multiple linear regression models with the issue of multicollinearity. We proposed the estimator by modifying the Liu estimator (LE) with the use of the existing modified ridge-type estimator and provide its properties. Performance of the proposed

Two modified ranked set sampling schemes are used to estimate the upper and lower quantiles of an underlying distribution. The performance of these sampling schemes is compared with that of ordinary ranked set sampling in view of Pitman’s measure of closeness criterion. Actually, a way of choosing estimators is proposed in the paper based on Pitman closeness which demonstrates that the mentioned modifications

Intraclass correlation coefficient for data in a clustered study is traditionally estimated from a one-way random-effects model. This model assumes normality for the random cluster effect and the residual effect. When the normality assumption is questionable, we find that the estimated correlation could be much below the nominal level when data are highly skewed or data have low kurtosis. We develop

In this study, we first obtain likelihood based Wald and score tests for testing the homogeneity of scale parameters of k independent inverse Gaussian populations. After that, we adapt these tests into the computational approach test procedure. We also provide theoretical investigation of these proposed tests in detail. The performances of the proposed tests are compared with the existing tests via

In this article, we propose a family of flexible shrinkage estimators which can use geometric or arithmetic means of sample variances of all genes as the target statistic for shrinkage and also allow the non-normal distribution of the data. The optimal shrinkage parameters are derived under the squared log error loss function and estimated by consistent estimators under a general asymptotic framework

This article examines the performance of the adaptive cumulative sum (CUSUM) plans and three simultaneous CUSUM statistics with different levels of temporal memory for monitoring changes in dispersion in the steady state situation. This article demonstrates that taking a larger rational subgroup has early out-of-control detection advantages for all monitoring plans considered. The article illustrates

The Mann–Whitney (MW) test is one of the most important nonparametric tests used in the comparison of the location parameters of two populations. Unlike the t-test, the MW test can be used when the assumption of normality fails to hold. In this paper, the MW U statistic is used to construct two efficient distribution-free monitoring schemes, namely the mixed generally weighted moving average-cumulative

We focus on the construction of confidence intervals for the ratios of medians of two independent, log-normal distributions based on the normal approximation (NA) approach, the method of variance estimate recovery (MOVER), and the generalized confidence interval (GCI) approach. We also compare the performance of the three confidence intervals in terms of the coverage probabilities, and average lengths

The goodness of fit (GOF) test plays an important role to determine whether a data set comes from an assumed distribution (with possibly unknown parameters). Many approaches have been proposed to perform the goodness of fit test, such as Chi-square, Kolmogorov-Smirnov, Cramér-Von Mises, and Anderson Darling test statistics, but most of those are proposed for univariate data. In this paper, we propose

The paper deals with two issues. The first is testing the hypothesis that two dependent variables have a common variance. Currently, a heteroscedastic analog of the Morgan–Pitman test appears to perform relatively well. The paper demonstrates that when the marginal distributions differ in skewness, this method can be highly unsatisfactory. An explanation for this poor performance is provided. The second

In this article, a new method was practiced to form a model by using twofold Weibull mixture distribution in right-censored reliability data. The method depends on estimating the parameters of right-censored twofold Weibull mixture distribution in a most appropriate way to the data by using genetic algorithm techniques. The best model was tried to be found by using MSE, MAE and MAPE metrics, respectively

The half logistic-geometric (HLG) distribution derived here is a simple distribution with interesting properties and is therefore a useful distribution for analyzing lifetime data. Algorithm is derived for the computation of single and product moments of order statistics from this distribution. These recurrence relations enable the computation of means, variances and covariances of all order statistics

Inherently, the survival function of a distribution converges to zero as time approaches infinity. The medical interpretation of this definition is that all the patient populations are susceptible to the event of interest that is death or recurrence of the disease after being treated, which is not always the case. This is what prompted the idea of inputting some modification to the domain of the unknown

This article focuses on variable selection for varying coefficient models in the case of the number of covariates being larger than the sample size. Combining B-spline basis function approximations with the modified adaptive group lasso, we establish selection consistency, convergence rate and asymptotic normality. Our contribution is that the marginal nonparametric estimates are used as weights of

In this article, we introduce a new nonparametric Shewhart − type control chart based on rank statistics. The setup of the proposed distribution − free monitoring scheme is presented in detail, while a real data example is studied in order to shed light on the implementation of the new control chart. Explicit formulae for the basic characteristics of the proposed rank-based chart are deduced. A detailed

Frailty models are generally used to model heterogeneity and dependence between individuals. The distribution of the frailty variable is often assumed to be continuous. However, there are situations where a discretely-distributed frailty may be appropriate. On the other hand, when a cure rate is present in survival data, these continuous distributions may not be appropriate since individuals with long-term

Renewal reward processes measure the cumulative rewards up to a given time, and sometimes uncertainty and randomness exist at the same time due to the complexity of real world. This article considers the first hitting time for the renewal process with uncertain interarrival times and random rewards. Analytic representations and numerical algorithms for the chance distribution and expected value of

The EWMA-t chart is used to monitor the process mean as it is robust against errors in estimating the process standard deviation or changing standard deviation. In this paper, we propose a double EWMA-t (DEWMA-t) chart for monitoring the process mean, which is a natural extension of the EWMA-t chart. The idea is to first construct the t chart using the EWMA statistic, and then put it into another EWMA

The quadratic inference function approach is a popular method in the analysis of correlated data. The quadratic inference function is formulated based on multiple sets of score equations (or extended score equations) that over-identify the regression parameters of interest, and improves efficiency over the generalized estimating equations under correlation misspecification. In this note, we provide

The large availability of undesigned data, a by-product of chemical industrial research and manufacturing, makes it attractive the venturesome use of machine learning for its plug-and-play appeal in attempt to extract value out of this data. Often this type of data does not only reflect the response to controlled variation but also to that caused by random effects. Thus, machine learning based models

In reliability engineering, study the estimated risk of reliability parameters estimation is an important problem. This paper studies the E-Bayesian estimations and their E-posterior risk of the failure probability under different prior distributions. Moreover, the analytical expressions of E-posterior risk under different prior distributions of hyper parameters are obtained respectively. Monte Carlo

In this work, we extend standard likelihood-based procedures to the multivariate linear model using the scale mixtures of multivariate skew-normal-Cauchy distributions. A simple EM algorithm for iteratively computing maximum likelihood estimates is derived. The observed information matrix is computed analytically to account for standard errors. Some results are obtained from real and simulated datasets

In this work, the volatility processes of the double Heston model are extended to treat the long memory property of the volatility. In this article, we study our presented model as a new version of the two-factor stochastic volatility model that it is proposed to model the volatility by a mean reverting equation driven by fractional Brownian motion named fractional Cox–Ingersoll–Ross process. Next

Nonlinear mixed-effects models allow modeling repeated measures over time. The fixed effects of these models allow incorporating covariates, whereas the random effects reflect the multiple sources of heterogeneity and correlation between and within the units. To estimate the parameters of these models, it is necessary to use iterative processes, which can be done through different approaches, some