New Techniques for the Analysis of Absenteeism Data

Arbous, A. G.; Sichel, H. S.

doi:10.1093/biomet/41.1-2.77

Cited by 48 publications

(27 citation statements)

References 7 publications

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“…That is, we have observations designated in k+ categories. We give example below, which is adapted from Hassan & Bilal (2008) and relates to the number of absenteeism among shift workers in steel industry as reported in Arbous & Sichel (1954). The category 25+ actually stands for counts in categories (25-48) and are combined in that category with a frequency count of 16.…”

Section: The Extended Com-poisson (Ecomp) Distributionmentioning

confidence: 99%

Correcting for Non-Sum to 1 Estimated Probabilities in Applications of Discrete Probability Models to Count Data

Lawal¹

2017

IJSP

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In this paper, we examine some often ignored or assumed problems relating with fitting probability models to count data either exhibiting over, equi, or under dispersion. Of particular concern are last category truncated data, where most often, expected values in this last category are collapsed together so that the sum of the expected values sum to the sample size in the data. That is, so that k ∑ i=0m i = n, the sample size. We shall for illustrative purposes in this paper, consider the following distributions: the negative binomial (NB), the Inverse trinomial (IT), the hyper-Poisson (HP), the Quasinegative binomial (QNBD), the extended com-Poisson distribution (ECOMP) as well as the negative binomial-exponential distribution (NBGE).Though, we have restricted our discussion to these six distributions, other distributions may also be employed but the patterns are always the same, that is, the sum of the estimated probabilities does not equal 1.00 and consequently, the sum of the expected values is always less or equal (Poisson case only) the sample size in the observed data. We propose a common procedure to rectify this problem for both right truncated or non-truncated frequency count data exhibiting either excess zeros or regular frequency data.

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Section: The Extended Com-poisson (Ecomp) Distributionmentioning

confidence: 99%

Correcting for Non-Sum to 1 Estimated Probabilities in Applications of Discrete Probability Models to Count Data

Lawal¹

2017

IJSP

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“…The data in Table 5 was originally studied by Arbous and Sichel (1954) in an attempt to create a model that can describe the distribution of absences to a group of people in single-and double-exposure periods. The original data contains the number of absences, x-value, of 248 shift workers in the years 1947 and 1948.…”

Section: Number Of Absences By Shift-workersmentioning

confidence: 99%

“…The original data contains the number of absences, x-value, of 248 shift workers in the years 1947 and 1948. Arbous and Sichel (1954) used the negative binomial distribution (NBD) to fit the data. Gupta and Ong (2004) proposed a four-parameter generalized negative binomial distribution to fit the data and compared it to the NBD and the GPD.…”

Section: Number Of Absences By Shift-workersmentioning

confidence: 99%

The Kumaraswamy-geometric distribution

2014

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In this paper, the Kumaraswamy-geometric distribution, which is a member of the T-geometric family of discrete distributions is defined and studied. Some properties of the distribution such as moments, probability generating function, hazard and quantile functions are studied. The method of maximum likelihood estimation is proposed for estimating the model parameters. Two real data sets are used to illustrate the applications of the Kumaraswamy-geometric distribution.

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“…This example illustrates the bivariate N BIG distribution and we have analyzed absenteeism data which appears in Arbous and Sichel (1954) and Stein et al (1987). These data refer to the number of absences in 1947 and the number of absences in 1948 of a sample of 248 workers.…”

Section: Applicationsmentioning

confidence: 99%

“…Since modelling the claim frequency data is one of the most important areas in actuarial theory, we will apply the moment and maximum likelihood methods to fit observed claims distributions in Klugman et al (1998) and Simon (1961). In the bivariate case we have fitted absenteeism data in Arbous and Sichel (1954) and Stein et al (1987). Expected frequencies show a satisfactory goodness of fit.…”

Section: Introductionmentioning

confidence: 98%