scholarly journals Statistical analysis of the power sum of multiple correlated log-normal components

1993 ◽  
Vol 42 (1) ◽  
pp. 58-61 ◽  
Author(s):  
A. Safak
Keyword(s):  
Statistical Analysis ◽  
Power Sum ◽  
Log Normal

scholarly journals Statistical Analysis of Natural Events in the United States

1991 ◽  
Vol 21 (2) ◽  
pp. 253-276 ◽  
Author(s):  
Charles Levi ◽  
Christian Partrat
Keyword(s):  
United States ◽  
Statistical Analysis ◽  
Negative Binomial ◽  
The United States ◽  
Normal Model ◽  
Chi Square ◽  
Expected Frequency ◽  
Kolmogorov Smirnov ◽  
Anderson Darling ◽  
Log Normal

AbstractA statistical analysis is performed on natural events which can produce important damages to insurers. The analysis is based on hurricanes which have been observed in the United States between 1954 et 1986.At first, independence between the number and the amount of the losses is examined. Different distributions (Poisson and negative binomial for frequency and exponential, Pareto and lognormal for severity) are tested. Along classical tests as chi-square, Kolmogorov-Smirnov and non parametric tests, a test with weights on the upper tail of the distribution is used: the Anderson – Darling test.Confidence intervals for the probability of occurrence of a claim and expected frequency for different potential levels of claims are derived. The Poisson Log-normal model gives a very good fit to the data.

Statistical Analysis of Pit Dimensions for Pre-Corroded AA 7075-T6

2014 ◽  
Vol 906 ◽  
pp. 259-262 ◽  
Author(s):  
Yong Fang Huang ◽  
Li Jie Chen ◽  
Xu Bin Ye
Keyword(s):  
Statistical Analysis ◽  
Fatigue Cracks ◽  
Gumbel Distribution ◽  
Fatigue Tests ◽  
Specimen Surface ◽  
Aa 7075 ◽  
Corrosion Pits ◽  
Log Normal ◽  
Aluminum Alloy 7075 ◽  
Log Normal Distribution

Aluminum alloy 7075-T6 specimens were corroded in 3.5% NaCl solution for 120 hours and 240 hours, respectively. Morphology and dimensions of corrosion pits on specimen surface were inspected with white light confocal profiler. Statistical analysis shows that pit dimensions can be fitted well with log-normal and Gumbel distribution. After surface inspections, we performed high-cycle fatigue tests for the specimens. Fracture analysis shows that fatigue cracks initiate from single pit or two adjacent pits, and the crack-initiation pit shape and dimensions were examined with SEM. It is found that initiation pit dimensions can be well described with the log-normal distribution. Additionally, initiation pit dimensions are significantly larger than those measured on specimen surface before fatigue tests.

scholarly journals Analysis of uncertainty in the surgical department: durations, requests and cancellations

2019 ◽  
Vol 43 (6) ◽  
pp. 706 ◽  
Author(s):  
Belinda Spratt ◽  
Erhan Kozan ◽  
Michael Sinnott
Keyword(s):  
Statistical Analysis ◽  
Total Duration ◽  
Analytical Techniques ◽  
Planning And Scheduling ◽  
Surgical Department ◽  
Modelling Uncertainty ◽  
Rate Dependent ◽  
Planning Staff ◽  
Log Normal ◽  
Surgical Duration

Objective Analytical techniques are being implemented with increasing frequency to improve the management of surgical departments and to ensure that decisions are well informed. Often these analytical techniques rely on the validity of underlying statistical assumptions, including those around choice of distribution when modelling uncertainty. The aim of the present study was to determine a set of suitable statistical distributions and provide recommendations to assist hospital planning staff, based on three full years of historical data. Methods Statistical analysis was performed to determine the most appropriate distributions and models in a variety of surgical contexts. Data from 2013 to 2015 were collected from the surgical department at a large Australian public hospital. Results A log-normal distribution approximation of the total duration of surgeries in an operating room is appropriate when considering probability of overtime. Surgical requests can be modelled as a Poisson process with rate dependent on urgency and day of the week. Individual cancellations could be modelled as Bernoulli trials, with the probability of patient-, staff- and resource-based cancellations provided herein. Conclusions The analysis presented herein can be used to ensure that assumptions surrounding planning and scheduling in the surgical department are valid. Understanding the stochasticity in the surgical department may result in the implementation of more realistic decision models. What is known about the topic? Many surgical departments rely on crude estimates and general intuition to predict surgical duration, surgical requests (both elective and non-elective) and cancellations. What does this paper add? This paper describes how statistical analysis can be performed to validate common assumptions surrounding surgical uncertainty. The paper also provides a set of recommended distributions and associated parameters that can be used to model uncertainty in a large public hospital’s surgical department. What are the implications for practitioners? The insights on surgical uncertainty provided here will prove valuable for administrative staff who want to incorporate uncertainty in their surgical planning and scheduling decisions.

scholarly journals Sensitivity Analysis of the TOPSIS Method in Respect of Initial Data Distributions

2016 ◽  
Vol 55 (1) ◽  
pp. 45-51
Author(s):  
Rūta Simanavičienė ◽  
Vaida Petraitytė
Keyword(s):  
Decision Making ◽  
Sensitivity Analysis ◽  
Statistical Analysis ◽  
Initial Data ◽  
Beta Distribution ◽  
Probability Distributions ◽  
Data Distribution ◽  
Multiple Criteria Decision Making ◽  
Topsis Method ◽  
Log Normal

The present article investigates the sensitivity of the multiple criteria decision-making method TOPSIS in respectof attribute probability distributions. To carry out research, initial data – attribute values – were generated according to anormal, log-normal, uniform, and beta distributions. Decision matrixes were constructed from the generated data. Byapplying the TOPSIS method to the matrixes generated, result samples were received. A statistical analysis was conductedfor the results obtained, which revealed that the distributions of the initial data comply with the distributions of the resultsreceived by the TOPSIS method. According to the most common alternative rank value, it was ascertained that the TOPSISmethod is the most sensitive for data distribution according to beta distribution, and the least sensitive for data distributionaccording to lognormal distribution.

scholarly journals Shard weight: a new look at the numbers

2017 ◽  
Author(s):  
Stefano Costa
Keyword(s):  
Statistical Analysis ◽  
Normal Distribution ◽  
Social Value ◽  
Archaeological Interpretation ◽  
Log Normal ◽  
Log Normal Distribution ◽  
New Look

Weight of single ceramic sherds from archaeological contexts has a log-normal distribution. This property can be used for a purely quantitative statistical analysis of contexts. Rather than focusing on the economic and social value of ceramics, quantification is used to evaluate depositional histories and verify or improve archaeological interpretation.

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