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In this paper, we propose a cure rate survival model by assuming that the number of competing causes of the event of interest follows the Poisson distribution and the time to event has the Birnbaum–Saunders (BS) distribution. We define the Poisson BS distribution and provide two useful representations for its density function which facilitate to obtain some mathematical properties. Two closed-form expressions for the moments of the new distribution are given. We estimate the parameters of the model with cure rate using maximum likelihood. For different parameter settings, sample sizes and censoring percentages, several simulations are performed. We derive the appropriate matrices for assessing local influence on the parameter estimates under different perturbation schemes and present some ways to perform a global influence study. We analyse a real data set from the medical area. [ABSTRACT FROM PUBLISHER]

In this paper, we propose a modification of the multiple sinusoidal model such that periodic data observed with the presence of a trend component can be analysed. In particular, we work with a linear trend model. But it can be developed similarly for the polynomial trend and the frequency model also. We consider the problem of estimation of frequency and amplitude parameters observed with a linear trend and a stationary linear process. We apply the usual least-squares method to the differenced data. It has been proved that the proposed estimators are strongly consistent and asymptotically normally distributed. Extensive simulations have been reported to justify the suitability of the model and the proposed method. One real data set, the monthly airline passenger data, has been analysed using the model. It is observed that the proposed model fits the data marginally better than the traditional time-series model in terms of residual sums of squares. [ABSTRACT FROM PUBLISHER]

This paper presents a step-stress accelerated life test for two stress variables to obtain optimal hold times under a Type-I hybrid censoring scheme. An exponentially distributed life and a cumulative exposure model are assumed. The maximum-likelihood estimates are given, from which the asymptotic variance and the Fisher information matrix are obtained. The optimal test plan is determined for each combination of stress levels by minimizing the asymptotic variance of reliability estimate at a typical operating condition. Finally, simulation results are discussed to illustrate the proposed criteria. Simulation results show that the proposed optimum plan is robust, and the initial estimates have a small effect on optimal values. [ABSTRACT FROM AUTHOR]

In this paper, we develop a new forecasting algorithm for value-at-risk (VaR) based on ARMA-GARCH (autoregressive moving average-generalized autoregressive conditional heteroskedastic) models whose innovations follow a Gaussian mixture distribution. For the parameter estimation, we employ the conditional least squares and quasi-maximum-likelihood estimator (QMLE) for ARMA and GARCH parameters, respectively. In particular, Gaussian mixture parameters are estimated based on the residuals obtained from the QMLE of GARCH parameters. Our algorithm provides a handy methodology, spending much less time in calculation than the existing resampling and bias-correction method developed in Hartz et al. [Accurate value-at-risk forecasting based on the normal-GARCH model, Comput. Stat. Data Anal. 50 (2006), pp. 3032-3052]. Through a simulation study and a real-data analysis, it is shown that our method provides an accurate VaR prediction. [ABSTRACT FROM AUTHOR]

In ophthalmologic or otolaryngologic study, each subject may contribute paired organs measurements to the analysis. A number of statistical methods have been proposed on bilateral correlated data. In practice, it is important to detect confounding effect by treatment interaction, since ignoring confounding effect may lead to unreliable conclusion. Therefore, stratified data analysis can be considered to adjust the effect of confounder on statistical inference. In this article, we investigate and derive three test procedures for testing homogeneity of difference of two proportions for stratified correlated paired binary data in the basis of equal correlation model assumption. The performance of proposed test procedures is examined through Monte Carlo simulation. The simulation results show that the Score test is usually robust on type I error control with high power, and therefore is recommended among the three methods. One example from otolaryngologic study is given to illustrate the three test procedures. [ABSTRACT FROM AUTHOR]

While the concept of sampling variation is well understood by most researchers in the field of deception detection, previous studies have failed to account for the multiple sources of sampling variation present in typical experimental designs and use participant-level data as the dependant measure in analyses. These aggregated data, however, contain inherent biases that can mislead researchers. We argue that to appropriately test hypotheses and make inferences beyond a particular sample of participants, the decision-level data must be modelled directly. To illustrate how this can be achieved we provide an introduction to generalized linear mixed models (GLMMs) for the analysis of deception data and present Monte Carlo simulations demonstrating both the seriousness of the inherent biases present in participant-level data and the benefits of the GLMM approach. These simulations suggest that the empirical Type 1 and Type 2 error rates associated with main effects testing in deception research may be as high as 35% when data are aggregated ‘by-judge’ and as high as 60% when data are aggregated ‘by-sender’, respectively. When decision-level data are modelled directly, however, these rates are likely to be close to nominal levels (6% and 28%, respectively). Implications for past and future research are discussed. [ABSTRACT FROM PUBLISHER]

In multiple imputation (MI), the resulting estimates are consistent if the imputation model is correct. To specify the imputation model, it is recommended to combine two sets of variables: those that are related to the incomplete variable and those that are related to the missingness mechanism. Several possibilities exist, but it is not clear how they perform in practice. The method that simply groups all variables together into the imputation model and four other methods that are based on the propensity scores are presented. Two of them are new and have not been used in the context of MI. The performance of the methods is investigated by a simulation study under different missing at random mechanisms for different types of variables. We conclude that all methods, except for one method based on the propensity scores, perform well. It turns out that as long as the relevant variables are taken into the imputation model, the form of the imputation model has only a minor effect in the quality of the imputations. [ABSTRACT FROM PUBLISHER]

This article primarily aims to put forward the linearized restricted ridge regression (LRRR) estimator in linear regression models. Two types of LRRR estimators are investigated under the PRESS criterion and the optimal LRRR estimators and the optimal restricted generalized ridge regression estimator are obtained. We apply the results to the Hald data and finally make a simulation study by using the method of McDonald and Galarneau. [ABSTRACT FROM AUTHOR]

In this article, we propose three M-estimators for multiple regression model when response variable is subject to double censoring. The consistency of the proposed M-estimators is established. A simulation study is conducted to investigate the performance of the proposed estimators. Furthermore, the simple bootstrap methods are used to construct interval estimators. [ABSTRACT FROM PUBLISHER]

We investigate three interval estimators for binomial misclassification rates in a complementary Poisson model where the data are possibly misclassified: a Wald-based interval, a score-based interval, and an interval based on the profile log-likelihood statistic. We investigate the coverage and average width properties of these intervals via a simulation study. For small Poisson counts and small misclassification rates, the intervals can perform poorly in terms of coverage. The profile log-likelihood confidence interval (CI) is often proved to outperform the other intervals with good coverage and width properties. Lastly, we apply the CIs to a real data set involving traffic accident data that contain misclassified counts. [ABSTRACT FROM AUTHOR]

Although there has been significant research on modelling and learning user preferences for various types of objects, there has been relatively little work on the problem of representing and learning preferences over sets of objects. We introduce a representation language, DD-PREF, that balances preferences for particular objects with preferences about the properties of the set. Specifically, we focus on the depth of objects (i.e. preferences for specific attribute values over others) and on the diversity of sets (i.e. preferences for broad vs. narrow distributions of attribute values). The DD-PREF framework is general and can incorporate additional object- and set-based preferences. We describe a greedy algorithm, DD-Select, for selecting satisfying sets from a collection of new objects, given a preference in this language. We show how preferences represented in DD-PREF can be learned from training data. Experimental results are given for three domains: a blocks world domain with several different task-based preferences, a real-world music playlist collection, and rover image data gathered in desert training exercises. [ABSTRACT FROM AUTHOR]

Field data are requisite in order to set up a realistic reservoir model capable of predicting the dynamic field behavior during the development stage of an oilfield. Well testing is considered as one of the most useful methods for obtaining reservoir and wellbore data. Numerous analytical models are utilized in analyzing vertical-well pressure transient tests, however, horizontal-well transient tests analysis has been considered as a more difficult undertaking. In this article, well test data of a horizontal well are simulated for homogeneous isotropic and anisotropic reservoirs. The data are later used in order to develop an empirical correlation that ratifies the vertical reservoir permeability estimated by well testing. [ABSTRACT FROM AUTHOR]

A new method for detecting the parameter changes in generalized autoregressive heteroskedasticity GARCH (1,1) model is proposed. In the proposed method, time series observations are divided into several segments and a GARCH (1,1) model is fitted to each segment. The goodness-of-fit of the global model composed of these local GARCH (1,1) models is evaluated using the corresponding information criterion (IC). The division that minimizes IC defines the best model. Furthermore, since the simultaneous estimation of all possible models requires huge computational time, a new time-saving algorithm is proposed. Simulation results and empirical results both indicate that the proposed method is useful in analysing financial data. [ABSTRACT FROM AUTHOR]

Space-time correlation modelling is one of the crucial steps of traditional structural analysis, since space-time models are used for prediction purposes. A comparative study among some classes of space-time covariance functions is proposed. The relevance of choosing a suitable model by taking into account the characteristic behaviour of the models is proved by using a space-time data set of ozone daily averages and the flexibility of the product-sum model is also highlighted through simulated data sets. [ABSTRACT FROM AUTHOR]