A comprehensive account of economic size distributions around the world and throughout the years In the course of the past 100 years, economists and applied statisticians have developed a remarkably diverse variety of income distribution models, yet no single resource convincingly accounts for all of these models, analyzing their strengths and weaknesses, similarities and differences. Statistical Size Distributions in Economics and Actuarial Sciences is the first collection to systematically investigate a wide variety of parametric models that deal with income, wealth, and related notions. Christian Kleiber and Samuel Kotz survey, compliment, compare, and unify all of the disparate models of income distribution, highlighting at times a lack of coordination between them that can result in unnecessary duplication. Considering models from eight languages and all continents, the authors discuss the social and economic implications of each as well as distributions of size of loss in actuarial applications. Specific models covered include: Pareto distributions Lognormal distributions Gamma-type size distributions Beta-type size distributions Miscellaneous size distributions Three appendices provide brief biographies of some of the leading players along with the basic properties of each of the distributions. Actuaries, economists, market researchers, social scientists, and physicists interested in econophysics will find Statistical Size Distributions in Economics and Actuarial Sciences to be a truly one-of-a-kind addition to the professional literature.

Statistical Size Distributions in Economics and Actuarial Sciences

Generalized Rayleigh distribution

Evolution of insect resistance to transgenic crops containing Bacillus thuringiensis (Bt) genes is a serious threat to the sustainability of this technology. However, field resistance related to the reduced efficacy of Bt maize has not been documented in any lepidopteran pest in the mainland U.S. after 18 years of intensive Bt maize planting. Here we report compelling evidence of field resistance in the fall armyworm, Spodoptera frugiperda (J.E. Smith), to Cry1F maize (TC 3507) in the southeastern region of the U.S. An F2 screen showed a surprisingly high (0.293) Cry1F resistance allele frequency in a population collected in 2011 from non-Bt maize in south Florida. Field populations from non-Bt maize in 2012–2013 exhibited 18.8-fold to >85.4-fold resistance to purified Cry1F protein and those collected from unexpectedly damaged Bt maize plants at several locations in Florida and North Carolina had >85.4-fold resistance. In addition, reduced efficacy and control failure of Cry1F maize against natural populations of S. frugiperda were documented in field trials using Cry1F-based and pyramided Bt maize products in south Florida. The Cry1F-resistant S. frugiperda also showed a low level of cross-resistance to Cry1A.105 and related maize products, but not to Cry2Ab2 or Vip3A. The occurrence of Cry1F resistance in the U.S. mainland populations of S. frugiperda likely represents migration of insects from Puerto Rico, indicating the great challenges faced in achieving effective resistance management for long-distance migratory pests like S. frugiperda.

/pdf/cry1f-resistance-in-fall-armyworm-spodoptera-frugiperda-fxsmrvkmxw.pdf

Cry1F Resistance in Fall Armyworm Spodoptera frugiperda: Single Gene versus Pyramided Bt Maize

Objective This study had 3 goals, which were to examine the following: the frequency of atypical development, consistent with the broader autism phenotype, in high-risk infant siblings of children with autism spectrum disorder (ASD); the age at which atypical development is first evident; and which developmental domains are affected. Method A prospective longitudinal design was used to compare 294 high-risk infants and 116 low-risk infants. Participants were tested at 6, 12, 18, 24, and 36 months of age. At the final visit, outcome was classified as ASD, Typical Development (TD), or Non-TD (defined as elevated Autism Diagnostic Observation Schedule [ADOS] score, low Mullen Scale scores, or both). Results Of the high-risk group, 28% were classified as Non-TD at 36 months of age. Growth curve models demonstrated that the Non-TD group could not be distinguished from the other groups at 6 months of age, but differed significantly from the Low-Risk TD group by 12 months on multiple measures. The Non-TD group demonstrated atypical development in cognitive, motor, language, and social domains, with differences particularly prominent in the social-communication domain. Conclusions These results demonstrate that features of atypical development, consistent with the broader autism phenotype, are detectable by the first birthday and affect development in multiple domains. This highlights the necessity for close developmental surveillance of infant siblings of children with ASD, along with implementation of appropriate interventions as needed.

/pdf/the-broader-autism-phenotype-in-infancy-when-does-it-emerge-134qip2qk9.pdf

The Broader Autism Phenotype in Infancy: When Does It Emerge?

The present review with focus on the last decade (2000–2010) aims to (i) collecting the major hypotheses explaining freshwater biodiversity patterns, (ii) identifying the main stressors affecting freshwater biodiversity, and (iii) revealing information gaps regarding ecosystem types, organism groups, spatial and temporal scales to highlight research needs to better propose sound conservation measures. The comparative analysis addresses six organism groups ranging from microorganisms to fish in basins, rivers, lakes, wetlands, ponds and groundwater. Short-term studies at ecoregion and catchment scale focusing on invertebrates, macrophytes and fish in Palaearctic and Nearctic regions dominated. The most frequent hypotheses tested were the landscape filter concept, the species–area relationship, the metacommunity concept. Dominating natural drivers were area, heterogeneity and disturbance. Land use, eutrophication and habitat destruction were identified as most important stressors. Generally, freshwater biodiversity declined in response to these stressors in contrast to increasing biodiversity determined by natural drivers across all ecosystems. Preferred organism groups were fish and invertebrates, most frequently studied in rivers, in contrast to smaller organisms (e.g. bacteria) and, e.g. groundwater being underrepresented. Hypotheses originating from the last century are still tested in freshwater research, while novel concepts are either missing or untested. Protection of freshwater biodiversity is the ultimate challenge since it supports valuable ecosystems services ensuring perpetuation of mankind. For that, comprehensive large-scale studies with holistic approaches are urgently needed.

Drivers and stressors of freshwater biodiversity patterns across different ecosystems and scales: a review

SAS® software is a comprehensive set of integrated tools and solutions for accessing, managing, and analyzing data. SAS, which was formed as a company in 1976, is a leading developer of statistical software, which is widely used in academic, business, and government organizations. Since the 1980s, SAS has expanded its analytical software to include forecasting and econometrics, data mining, text mining, and operations research. SAS now builds on these components to provide software for business analytics and solutions for industry‐specific problems such as customer intelligence, fraud prevention, and risk management. This article describes the evolution of SAS as a company and overviews new directions in its analytical software. An example program illustrates key elements of SAS programming that are useful for statistical analysis. WIREs Comp Stat 2011 3 1–11 DOI: 10.1002/wics.131

To the Editor: Colorectal cancer (CRC) is a leading cause of cancer death.1 Current guidelines for CRC screening recommend universal screening for adults ages 50-75,1 From ages 76-85, screening decisions should be based on health status and personal values. After age 85, screening is not recommended given lack of benefit.1,2 However, studies of adults living independently and in nursing homes have shown inappropriate CRC screening(overuse and underuse) in all age ranges.3-5 Whether CRC screening rates for individuals in assisted living (AL) communities—who are less impaired than those in nursing homes—are inappropriate is unknown. To explore CRC screening in AL, we conducted interviews and chart review of a cross-sectional sample in two AL communities in central North Carolina.

/pdf/a-pilot-study-to-examine-colorectal-cancer-screening-in-two-4bxnjxn8l7.pdf

A Pilot Study to Examine Colorectal Cancer Screening in Two Assisted Living Communities

SUMMARY A set of families of distributions which might be useful for fitting data was described by Burr (1942). Special attention was focused on the family, Type XII, with generic distribution function 1- (1 + Xc)-k (X > 0) which yields a wide range of values of skewness, 1fl1, and kurtosis, fi2. The area in the (Ifi1, /2) plane corresponding to the Type XIT distributions is derived and presented in two figures. 1. INTRODIUCTION Suppose that Z is a positive random variable with probability density function

/pdf/a-guide-to-the-burr-type-xii-distributions-26u3sgvzdk.pdf

A guide to the Burr type XII distributions

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A Pilot Study to Examine Colorectal Cancer Screening in Two Assisted Living Communities

A guide to the Burr type XII distributions