Contributions to the Theory of Accident Proneness. 2. True or False Contagion,

Abstract

The first part of the present paper 11 was concerned with the theoretical aspect of the following practical question: can one use the number of light accidents incurred by different individuals in the past to predict the number of severe accidents in a hazardous occupation to be sustained in the future? The theoretical assumptions underlying this study form an extension of the well-known scheme due to Greenwood, Yule, and Newbold. An essential part of this scheme is characterized by the postulates: (1) that the individuals of a population differ from each other in accident proneness, (2) that the accidents already incurred do not change the probabilities of further accidents in the future, and (3) that these probabilities stay constant in time and are not modified by the experience that the individual may gain in the particular occupations. These three postulates may be symbolized by the combined term 'mixture-no contagion-no time-effect model.' In order to be able to deal with two kinds of accidents, light and severe, the above three postulates were supplemented by two more; (4) that the expected number u of light accidents per unit of time is proportional to the expected number lambda of severe accidents (this postulate was termed the fundamental hypothesis), and (5) that to each severe accident there corresponds a fixed probability phase that the individual involved in the accident will survive.

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Document Details

Document Type
Technical Report
Publication Date
Jan 01, 1952
Accession Number
ADA311362

Entities

People

  • Grace E. Bates
  • Jerzy Neyman

Organizations

  • University of California, Berkeley

Tags

Communities of Interest

  • Energy and Power Technologies
  • Human Systems

DTIC Thesaurus Topics

  • Accidents
  • California
  • Distribution Functions
  • Human Behavior
  • Intervals
  • Mathematical Models
  • Models
  • Observation
  • Personnel Management
  • Probability
  • Probability Density Functions
  • Random Variables
  • Statistical Data
  • Statistics
  • Time Intervals
  • United States
  • Universities

Readers

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  • Statistical inference.
  • Theoretical Analysis.