scholarly journals Median estimation of chemical constituents for sampling on two occasions under a log‐normal model

2015 ◽  
Vol 57 (5) ◽  
pp. 914-929
Author(s):  
Athanassios Kondylis
Keyword(s):  
Chemical Constituents ◽  
Normal Model ◽  
Log Normal

scholarly journals Dependence modelling in multivariate claims run-off triangles

2012 ◽  
Vol 7 (1) ◽  
pp. 3-25 ◽  
Author(s):  
Michael Merz ◽  
Mario V. Wüthrich ◽  
Enkelejd Hashorva
Keyword(s):  
Closed Form ◽  
Central Issue ◽  
Normal Model ◽  
Closed Form Solutions ◽  
Prediction Uncertainty ◽  
Run Off ◽  
Classical Models ◽  
Dependence Modelling ◽  
Log Normal ◽  
Dependence Structures

AbstractA central issue in claims reserving is the modelling of appropriate dependence structures. Most classical models cannot cope with this task. We define a multivariate log-normal model that allows to model both, dependence between different sub-portfolios and dependence within sub-portfolios such as claims inflation. In this model we derive closed form solutions for claims reserves and the corresponding prediction uncertainty.

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.

scholarly journals Bayesian CMB foreground separation with a correlated log-normal model

2014 ◽  
Vol 10 (S306) ◽  
pp. 16-18
Author(s):  
Niels Oppermann ◽  
Torsten A. Enßlin
Keyword(s):  
Power Spectrum ◽  
Bayesian Methods ◽  
Natural Extension ◽  
Superior Performance ◽  
Normal Model ◽  
Spatial Correlations ◽  
Angular Power Spectrum ◽  
Cross Correlations ◽  
Log Normal ◽  
Frequency Behavior

AbstractThe extraction of foreground and CMB maps from multi-frequency observations relies mostly on the different frequency behavior of the different components. Existing Bayesian methods additionally make use of a Gaussian prior for the CMB whose correlation structure is described by an unknown angular power spectrum. We argue for the natural extension of this by using non-trivial priors also for the foreground components. Focusing on diffuse Galactic foregrounds, we propose a log-normal model including unknown spatial correlations within each component and cross-correlations between the different foreground components. We present case studies at low resolution that demonstrate the superior performance of this model when compared to an analysis with flat priors for all components.

A Generalized Log-Normal Model for Grouped Survival Data

2010 ◽  
Vol 39 (15) ◽  
pp. 2659-2666 ◽  
Author(s):  
Liciana V. A. Silveira ◽  
Enrico A. Colosimo ◽  
José Raimundo de S. Passos
Keyword(s):  
Survival Data ◽  
Normal Model ◽  
Log Normal

Modeling the Uncertainty of Surgical Procedure Times

2000 ◽  
Vol 92 (4) ◽  
pp. 1160-1167 ◽  
Author(s):  
David P. Strum ◽  
Jerrold H. May ◽  
Luis G. Vargas
Keyword(s):  
Surgical Procedure ◽  
Goodness Of Fit ◽  
Statistical Tests ◽  
Current Procedural Terminology ◽  
Normal Model ◽  
Rounding Errors ◽  
Goodness Of Fit Tests ◽  
Time Estimates ◽  
Surgical Scheduling ◽  
Log Normal

Background Medical institutions are under increased economic pressure to schedule elective surgeries efficiently to contain the costs of surgical services. Surgical scheduling is complicated by variability inherent in the duration of surgical procedures. Modeling that variability, in turn, provides a mechanism to generate accurate time estimates. Accurate time estimates are important operationally to improve operating room utilization and strategically to identify surgeons, procedures, or patients whose duration of surgeries differ from what might be expected. Methods The authors retrospectively studied 40,076 surgical cases (1,580 Current Procedural Terminology-anesthesia combinations, each with a case frequency of five or more) from a large teaching hospital, and attempted to determine whether the distribution of surgical procedure times more closely fit a normal or a log-normal distribution. The authors tested goodness-of-fit to these data for both models using the Shapiro-Wilk test. Reasons, in practice, the Shapiro-Wilk test may reject the fit of a log-normal model when in fact it should be retained were also evaluated. Results The Shapiro-Wilk test indicates that the log-normal model is superior to the normal model for a large and diverse set of surgeries. Goodness-of-fit tests may falsely reject the log-normal model during certain conditions that include rounding errors in procedure times, large sample sizes, untrimmed outliers, and heterogeneous mixed populations of surgical procedure times. Conclusions The authors recommend use of the log-normal model for predicting surgical procedure times for Current Procedural Terminology-anesthesia combinations. The results help to legitimize the use of log transforms to normalize surgical procedure times before hypothesis testing using linear statistical models or other parametric statistical tests to investigate factors affecting the duration of surgeries.

Application of the log-normal model for long term high affinity antibody/antigen interactions using Bio-Layer Interferometry

2013 ◽  
Vol 52 (2) ◽  
pp. 575-587 ◽  
Author(s):  
Jakob Wallner ◽  
Manfred Kühleitner ◽  
Norbert Brunner ◽  
Gabriele Lhota ◽  
Karola Vorauer-Uhl
Keyword(s):  
Normal Model ◽  
High Affinity ◽  
Log Normal
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