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Year | Authors | Title | Volume:Pages | ||
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2016 | Morteza Amini and S.M.T.K. MirMostafaee |
Interval Prediction of Order Statistics Based on Records by Employing Inter-Record Times: A Study Under Two Parameter Exponential Distribution | 13(1):1-15 |
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In this note, we propose a parametric inferential procedure for predicting future order statistics, which takes inter-record times into account. We utilize the additional information contained in inter-record times for predicting future order statistics on the basis of observed record values from an independent sample. The two parameter exponential distribution is assumed to be the underlying distribution. Download the paper | |||||

2016 | Anindita Datta, Seema Jaggi, Cini Varghese and Eldho Varghese |
Series of Incomplete Row-Column Designs with Two Units per Cell | 13(1):17-25 |
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Here, two series of incomplete row-column designs with two units per cell have been developed that are structurally complete, i.e. all the cells corresponding to the intersection of row and column receive two distinct treatments. Properties of these classes of designs have been studied and the methods result in designs in which the elementary contrasts of treatment effects are estimated with same variance. Download the paper | |||||

2016 | Jose Pina-Sanchez |
Adjustment of Recall Errors in Duration Data Using SIMEX | 13(1):27-58 |
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It is widely accepted that due to memory failures retrospective survey questions tend to be prone to measurement error. However, the proportion of studies using such data that attempt to adjust for the measurement problem is shockingly low. Arguably, to a great extent this is due to both the complexity of the methods available and the need to access a subsample containing either a gold standard or replicated values. Here I suggest the implementation of a version of SIMEX capable of adjusting for the types of multiplicative measurement errors associated with memory failures in the retrospective report of durations of life-course events. SIMEX is a method relatively simple to implement and it does not require the use of replicated or validation data so long as the error process can be adequately specified. To assess the effectiveness of the method I use simulated data. I create twelve scenarios based on the combinations of three outcome models (linear, logit and Poisson) and four types of multiplicative errors (non-systematic, systematic negative, systematic positive and heteroscedastic) affecting one of the explanatory variables. I show that SIMEX can be satisfactorily implemented in each of these scenarios. Furthermore, the method can also achieve partial adjustments even in scenarios where the actual distribution and prevalence of the measurement error differs substantially from what is assumed in the adjustment, which makes it an interesting sensitivity tool in those cases where all that is known about the error process is reduced to an educated guess. Download the paper | |||||

2016 | Janez Stare and Delphine Maucort-Boulch |
Odds Ratio, Hazard Ratio and Relative Risk | 13(1):59-67 |
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Odds ratio (OR) is a statistic commonly encountered in professional or scientific medical literature. Most readers perceive it as relative risk (RR), although most of them do not know why that would be true. But since such perception is mostly correct, there is nothing (or almost nothing) wrong with that. It is nevertheless useful to be reminded now and then what is the relation between the relative risk and the odds ratio, and when by equating the two statistics we are sometimes forcing OR to be something it is not. Another statistic, which is often also perceived as a relative risk, is the hazard ratio (HR). We encounter it, for example, when we fit the Cox model to survival data. Under proportional hazards it is probably “natural” to think in the following way: if the probability of death in one group is at every time point k-times as high as the probability of death in another group, then the relative risk must be k, regardless of where in time we are. This could be hardly further from the truth and in this paper we try to dispense with this blunder. Download the paper | |||||

2016 | Emilio Gomez–Deniz and Enrique Calderin |
The Mixture Poisson Exponential–Inverse Gaussian Regression Model: An application in Health Services | 13(2):71-85 |
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In this paper a mixed Poisson regression model for count data is introduced. This model is derived by mixing the Poisson distribution with the one–parameter continuous exponential–inverse Gaussian distribution. The obtained probability mass function is over–dispersed and unimodal with modal value located at zero. Estimation is performed by maximum likelihood. As an application, the demand for health services among people 65 and over is examined using this regression model since empirical evidence has suggested that the over–dispersion and a large portion of non–users are common features of medical care utilization data. Download the paper Download the supplementary information (computer code) | |||||

2016 | Kristina Veljkovic |
X bar control chart for non-normal symmetric distributions | 13(2):87-100 |
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In statistical quality control, X bar control chart is extensively used to monitor a change in the process mean. In this paper, X bar control chart for non-normal symmetric distributions is proposed. For chosen Student, Laplace, logistic and uniform distributions of quality characteristic, we calculated theoretical distribution of standardized sample mean and fitted Pearson type II or type VII distributions. Width of control limits and power of the X bar control chart were established, giving evidence of the goodness of fit of the corresponding Pearson distribution to the theoretical distribution of standardized sample mean. For implementation of X bar control chart in practice, numerical example of construction of a proposed chart is given. Download the paper Download the supplementary information (R code) | |||||

2016 | Marta Ferreira |
Estimating th e Coefficient of Asymptotic Tail Independence: a Comparison of Methods | 13(2):101-116 |
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Many multivariate analyses require the account of extreme events. Correlation is an insufficient measure to quantify tail dependence. The most common tail dependence coefficients are based on the probability of simultaneous exceedances. The coefficient of asymptotic tail independence introduced in Ledford and Tawn ([18] 1996) is a bivariate measure often used in the tail modeling of data in finance, environment, insurance, among other fields of applications. It can be estimated as the tail index of the minimum component of a random pair with transformed unit Pareto marginals. The literature regarding the estimation of the tail index is extensive. Semi-parametric inference requires the choice of the number k of the largest order statistics that lead to the best estimate, where there is a tricky trade-off between variance and bias. Many methodologies have been developed to undertake this choice, most of them applied to the Hill estimator (Hill, [16] 1975). We are going to analyze, through simulation, some of these methods within the estimation of the coefficient of asymptotic tail independence. We also compare with a minimumvariance reduced-bias Hill estimator presented in Caeiro et al. ([3] 2005). A pure heuristic procedure adapted from Frahm et al. ([13] 2005), used in a different context but with a resembling framework, will also be implemented. We will see that some of these simple tools should not be discarded in this context. Our study will be complemented by applications to real datasets Download the paper | |||||

2016 | Wararit Panichkitkosolkul |
Approximate Confidence Interval for the Reciprocal of a Normal Mean with a Known Coefficient of Variation | 13(2):117-130 |
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An approximate confidence interval for the reciprocal of a normal population mean with a known coefficient of variation is proposed. This has applications in the area of nuclear physics, agriculture and economic when the researcher knows the coefficient of variation. The proposed confidence interval is based on the approximate expectation and variance of the estimator by Taylor series expansion. A Monte Carlo simulation study was conducted to compare the performance of the proposed confidence interval with the existing confidence interval. Simulation results show that the proposed confidence interval performs as well as the existing one in terms of coverage probability. However, the approximate confidence interval is very easy to calculate compared with the exact confidence interval. Download the paper | |||||

2016 | Gloria Mateu-Figueras, Josep Daunis-i-Estadella, Germa Coenders,
Berta Ferrer-Rosell, Ricard Serlavos, Joan Manuel Batista-Foguet |
Exploring the Relationship between two Compositions using Canonical Correlation Analysis | 13(2):131-150 |
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The aim of this article is to describe a method for relating two compositions which combines compositional data analysis and canonical correlation analysis (CCA), and to examine its main statistical properties. We use additive log-ratio (alr) transformation on both compositions and apply standard CCA to the transformed data. We show that canonical variates are themselves log-ratios and log-contrasts. The first pair of canonical variates can be interpreted as the log-contrast of a composition that has the maximum correlation with a log-contrast of the other composition. The second pair can be interpreted as the log-contrast of a composition that has the maximum correlation with a log-contrast of the other composition, under the restriction that they are uncorrelated with the first pair, and so on. Using properties from changes of basis, we prove that both canonical correlations and canonical variates are invariant to the choice of divisors in alr transformation. We show how to implement the analysis and interpret the results by means of an illustration from the social sciences field using data from Kolb’s Learning Style Inventory and Boyatzis’ Philosophical Orientation Questionnaire, which distribute a fixed total score among several learning modes and philosophical orientations. Download the paper |