Cumulative Frequency Functions

Burr, Irving W.

doi:10.1214/aoms/1177731607

Cited by 1,155 publications

(652 citation statements)

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“…Assuming ε follows a complementary log log distribution (F (xβ) = 1 − exp[− exp(xβ)]) leads to the CLL model. The Burr-10 distribution (Burr 1942) assumes ε is distributed with cumulative distribution function F (xβ, α) = 1−1/ {1 + exp (xβ)} α…”

Section: Robustness Of Model Selectionmentioning

confidence: 99%

Waiting Patiently: An Empirical Study of Queue Abandonment in an Emergency Department

2015

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We study queue abandonment from a hospital emergency department. We show that abandonment is not only in uenced by wait time, but also by the queue length and the observable queue ows during the waiting exposure. For example, observing an additional person in the queue or an additional arrival to the queue leads to an increase in abandonment probability equivalent to a fteen minute or nine minute increase in wait time respectively. We also show that patients are sensitive to being "jumped" in the line and that patients respond di erently to people more sick and less sick moving through the system. This customer response to visual queue elements is not currently accounted for in most queuing models. Additionally, to the extent the visual queue information is misleading or does not lead to the desired behavior, managers have an opportunity to intervene by altering what information is available to waiting customers.

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Section: Robustness Of Model Selectionmentioning

confidence: 99%

Waiting Patiently: An Empirical Study of Queue Abandonment in an Emergency Department

2015

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“…Burr [18] developed a density function that can cover a wide range of various normal and non-normal distributions. Durling et al [24] proposed a standard bivariate Burr distribution in which the joint probability distribution of X and Y obey the bivariate Burr distribution, Burr (c, d, p).…”

Section: Literature Review---burr Distributionmentioning

confidence: 99%

“…For example, the normal density function can be approximated by Burr's [18] density function with c = 4.85437 and p = 6.22665. From Johnson and Kotz [25], we have the following results:…”

Section: Literature Review---burr Distributionmentioning

confidence: 99%

“…Burr [18] developed a density function which can represent various probability distributions based on the first four moments of the data. That is, if the mean, standard deviation, skewness coefficient and kurtosis coefficient of the process characteristic can be estimated with reasonably accuracy, then Burr's density function may be applied to fit this data set.…”

Section: Introductionmentioning

confidence: 99%

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Economic Design of Process Mean, Standard Deviation and Screening Limits Based on Burr Distribution

Chen

2017

AUSMT

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Abstract:The traditional quality screening problem considers the correlated and normal quality characteristics between product and screening characteristics. However, non-normal characteristics may exist in many practical production and inspection processes. In this paper, the author applies the bivariate Burr's density function in the quality level setting and screening problem to obtain the optimal process mean, standard deviation, and screening limits based on the minimization of the expected total product cost including quality loss for conforming products and rework costs for non-conforming products. A numerical example is given for illustration and a sensitivity analysis is conducted to assess the effects of model parameters on the optimal problem solution.

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“…The q-Weibull distribution has been used to numerically adjust a variety of PDFs, but, to the best of my knowledge, no dynamical basis has been presented so far. Distribution (24) belongs to the Burr class of probability density functions [22]. For small values of |v|, q W (v) in Eq.…”

Section: B a Generalised Weibull Distributionmentioning

confidence: 99%

On superstatistical multiplicative-noise processes

Queirós¹

2008

Braz. J. Phys.

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In this article we analyse the long-term probability density function of non-stationary dynamical processes with time varying multiplicative noise exponents which are enclosed inwards the Feller class of processes. The update in the value of the exponent occurs in the same conditions as presented by BECK and COHEN for superstatistics. Moreover, we are able to provide a dynamical scenario for the emergence of a generalisation of the Weibull distribution previously introduced.

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Cumulative Frequency Functions

Cited by 1,155 publications

References 0 publications

Waiting Patiently: An Empirical Study of Queue Abandonment in an Emergency Department

Waiting Patiently: An Empirical Study of Queue Abandonment in an Emergency Department

Economic Design of Process Mean, Standard Deviation and Screening Limits Based on Burr Distribution

On superstatistical multiplicative-noise processes

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