On a Class of Symmetric Nonnormal Distributions with a Kurtosis of Three

1996 ◽  
pp. 55-63 ◽  
Author(s):  
B. K. Kale ◽  
G. Sebastian
Keyword(s):  
Nonnormal Distributions

Estimation in bivariate nonnormal distributions with stochastic variance functions

2008 ◽  
Vol 52 (3) ◽  
pp. 1728-1745 ◽  
Author(s):  
Moti L. Tiku ◽  
M. Qamarul Islam ◽  
Hakan S. Sazak
Keyword(s):  
Nonnormal Distributions ◽  
Variance Functions

scholarly journals Distribution of Xanthomonas hortorum pv. carotae Populations in Naturally Infested Carrot Seed Lots

Plant Disease ◽  
2020 ◽  
Vol 104 (8) ◽  
pp. 2144-2148 ◽  
Author(s):  
Jeness C. Scott ◽  
Jeremiah K. S. Dung
Keyword(s):  
Daucus Carota ◽  
Bacterial Blight ◽  
Limit Of Detection ◽  
Plant Pathogenic Bacterium ◽  
Nonnormal Distributions ◽  
Individual Seed ◽  
Seed Health ◽  
Seedborne Disease ◽  
Carrot Seed ◽  
The Mean

Bacterial blight of carrot (Daucus carota subsp. sativus), caused by the plant-pathogenic bacterium Xanthomonas hortorum pv. carotae, is a common seedborne disease of carrot wherever the crop is grown. Carrot seed lots were evaluated to determine the variability and distribution of populations of X. hortorum pv. carotae among individual carrot seeds. Twenty-four carrot seed lots harvested between 2014 and 2016 were subjected to a bulk seed wash dilution-plate assay to obtain mean X. hortorum pv. carotae levels. Mean infestation levels resulting from the bulk seed wash assays among the 24 seed lots ranged from 1.2 × 107 and 9.6 × 108 CFU/g seed and averaged 3.6 × 108 CFU/g seed. Individual seeds from the same 24 lots were also tested with a scaled-down wash assay of individual seeds. Among the 1,380 seeds that were individually assayed, 475 X. hortorum pv. carotae-positive seeds were detected (34.4%). Rates of X. hortorum pv. carotae detection on individual seed in seed lots ranged from 0% (not detected) to 97.9%, and the mean and median X. hortorum pv. carotae population on an individual seed was 8.3 × 104 and 6.3 × 101 CFU/seed, respectively. Among individual seeds, X. hortorum pv. carotae populations ranged from 2 (the limit of detection of the assay) to 3.6 × 107 CFU/seed. CFU data for 23 of the 24 seed lots were nonnormal and the Log-Logistic (3P) distribution best described populations of X. hortorum pv. carotae recovered from individual carrot seeds. The influence and impact of nonnormal distributions of X. hortorum pv. carotae in commercial carrot seed lots on seed health tests, seedborne transmission, and bacterial blight epidemiology requires further study.

scholarly journals A Comparison of Different Nonnormal Distributions in Growth Mixture Models

2019 ◽  
Vol 79 (3) ◽  
pp. 577-597
Author(s):  
Sookyoung Son ◽  
Hyunjung Lee ◽  
Yoona Jang ◽  
Junyeong Yang ◽  
Sehee Hong
Keyword(s):  
Mixture Models ◽  
Outcome Variable ◽  
Normal Distributions ◽  
Nonnormal Distributions ◽  
Growth Mixture Models ◽  
Time Points ◽  
Growth Mixture ◽  
Distal Outcome ◽  
T Distribution ◽  
Skew Normal

The purpose of the present study is to compare nonnormal distributions (i.e., t, skew-normal, skew- t with equal skew and skew- t with unequal skew) in growth mixture models (GMMs) based on diverse conditions of a number of time points, sample sizes, and skewness for intercepts. To carry out this research, two simulation studies were conducted with two different models: an unconditional GMM and a GMM with a continuous distal outcome variable. For the simulation, data were generated under the conditions of a different number of time points (4, 8), sample size (300, 800, 1,500), and skewness for intercept (1.2, 2, 4). Results demonstrate that it is not appropriate to fit nonnormal data to normal, t, or skew-normal distributions other than the skew- t distribution. It was also found that if there is skewness over time, it is necessary to model skewness in the slope as well.

Weighted Simplex Procedures for Determining Boundary Points and Constants for the Univariate and Multivariate Power Methods

2000 ◽  
Vol 25 (4) ◽  
pp. 417-436 ◽  
Author(s):  
Todd C. Headrick ◽  
Shlomo S. Sawilowsky
Keyword(s):  
Standard Deviation ◽  
Lower Bound ◽  
Lagrange Multiplier ◽  
Sufficient Conditions ◽  
Efficient Algorithms ◽  
Data Sets ◽  
Transformation Techniques ◽  
Normal Distributions ◽  
Nonnormal Distributions ◽  
Necessary And Sufficient

The power methods are simple and efficient algorithms used to generate either univariate or multivariate nonnormal distributions with specified values of (marginal) mean, standard deviation, skew, and kurtosis. The power methods are bounded as are other transformation techniques. Given an exogenous value of skew, there is an associated lower bound of kurtosis. Previous approximations of the boundary for the power methods are either incorrect or inadequate. Data sets from education and psychology can be found to lie within, near, or outside tile boundary of the power methods. In view of this, we derived necessary and sufficient conditions using the Lagrange multiplier method to determine the boundary of the power methods. The conditions for locating and classifying modes for distributions on the boundary were also derived. Self-contained interactive Fortran programs using a Weighted Simplex Procedure were employed to generate tabled values of minimum kurtosis for a given value of skew and power constants for various (non)normal distributions.

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