M. Jabbari Nooghabi

Bio: M. Jabbari Nooghabi is an academic researcher from Ferdowsi University of Mashhad. The author has contributed to research in topics: Pareto distribution & Gamma distribution. The author has an hindex of 3, co-authored 5 publications receiving 45 citations.

Detecting Outliers in Gamma Distribution

Abstract: Zerbet and Nikulin presented the new statistic Z k for detecting outliers in exponential distribution. They also compared this statistic with Dixon's statistic D k . In this article, we extend this approach to gamma distribution and compare the result with Dixon's statistic. The results show that the test based on statistic Z k is more powerful than the test based on the Dixon's statistic.

On entropy of a Pareto distribution in the presence of outliers

Abstract: The aim of this paper is obtaining the amount of information there exists in the Pareto distribution in the presence of outliers For the sake of this purpose, Shannon entropy, ϵ-entropy, Fisher information, and Kullback–Leibler distance are computed Furthermore, a section has been devoted to compare these quantities in these two cases of the Pareto distribution (with outliers and the homogenous case) At the end of this paper, two actual examples, which are related to insurance companies, are brought A brief summary of which is done in this work is also reported

Process capability indices in normal distribution with the presence of outliers

Abstract: Process capability indices (PCIs) are useful measures to evaluate the performance and capability of a process when it is under control. Assuming the specification variable is distributed from a nor...

Detecting Outliers in Exponentiated Pareto Distribution

Abstract: In this paper, we use two statistics for detecting outliers in exponentiated Paretodistribution. These statistics are the extension of the statistics for detecting outliers inexponential and gamma distributions. In fact, we compare the power of our test statisticsbased on the simulation study and identify the better test statistic for detecting outliers inexponentiated Pareto distribution. At the end, we describe an example from insurancecompany.

Characterization Problems In Mathematical Statistics

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A Test Detecting the Outliers for Continuous Distributions Based on the Cumulative Distribution Function of the Data Being Tested

Abstract: One of the pillars of experimental science is sampling. Based on the analysis of samples, estimations for populations are made. There is an entire science based on sampling. Distribution of the population, of the sample, and the connection among those two (including sampling distribution) provides rich information for any estimation to be made. Distributions are split into two main groups: continuous and discrete. The present study applies to continuous distributions. One of the challenges of sampling is its accuracy, or, in other words, how representative the sample is of the population from which it was drawn. To answer this question, a series of statistics have been developed to measure the agreement between the theoretical (the population) and observed (the sample) distributions. Another challenge, connected to this, is the presence of outliers - regarded here as observations wrongly collected, that is, not belonging to the population subjected to study. To detect outliers, a series of tests have been proposed, but mainly for normal (Gauss) distributions—the most frequently encountered distribution. The present study proposes a statistic (and a test) intended to be used for any continuous distribution to detect outliers by constructing the confidence interval for the extreme value in the sample, at a certain (preselected) risk of being in error, and depending on the sample size. The proposed statistic is operational for known distributions (with a known probability density function) and is also dependent on the statistical parameters of the population—here it is discussed in connection with estimating those parameters by the maximum likelihood estimation method operating on a uniform U(0,1) continuous symmetrical distribution.

Evaluation and comparison of estimations in the generalized exponential-Poisson distribution

Abstract: The uniformly minimum variance unbiased, maximum-likelihood, percentile and least-squares estimators of the probability density function and the cumulative distribution function are derived for the generalized exponential-Poisson distribution. This model has shown to be useful in reliability and lifetime data modelling, especially when the hazard rate function has a bathtub shape. Simulation studies are also carried out to show that the maximum-likelihood estimator is better than the uniformly minimum variance unbiased estimator (UMVUE) and that the UMVUE is better than others.

Revision Analysis of Robotic Arm-Assisted and Manual Unicompartmental Knee Arthroplasty.

Abstract: Background The purpose of this study was to evaluate hospital admissions for revision surgeries associated with robotic arm–assisted unicompartmental knee arthroplasty (rUKA) vs manually instrumented UKA (mUKA) procedures. Methods Patients ≥18 years of age who received either a mUKA or a rUKA procedure were candidates for inclusion and were identified by the presence of appropriate billing codes. Procedures performed between March 1, 2013 and July 31, 2015 were used to calculate the rate of surgical revisions occurring within 24-months of the index procedure. Following propensity matching, 246 rUKA and 492 mUKA patients were included. Revision rates and the associated costs were compared between the two cohorts. The Mann-Whitney U test was used to compare continuous variables, and Fisher’s exact tests was used to analyze discrete categorical variables. Results At 24 months after the primary UKA procedure, patients who underwent rUKA had fewer revision procedures (0.81% [2/246] vs 5.28% [26/492]; P = .002), shorter mean length of stay (2.00 vs 2.33 days; P > .05), and incurred lower mean costs for the index stay plus revisions ($26,001 vs $27,915; P > .05) than mUKA patients. Length of stay at index and index costs were also lower for rUKA patients (1.77 vs 2.02 days; P = .0047) and ($25,786 vs $26,307; P > .05). Conclusions The study results demonstrate that patients who underwent rUKA had fewer revision procedures, shorter length of stay, and incurred lower mean costs (although not statistically different) during the index admission and at 24 months postoperatively. These results could be important for payers as the prevalence of end-stage knee osteoarthritis increases alongside the demand for cost-efficient treatments.