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2348 results in Statistical theory and methods

Page 19 of 118

  • 1 - Motivation

  • from Part I - Introduction
  • Nils B. Weidmann, Universität Konstanz, Germany
  • Book:
    Data Management for Social Scientists
    Published online:
    03 March 2023
    Print publication:
    09 March 2023, pp 3-13
    • Chapter
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  • 3 - Distributional Extensions and Generalities

  • Kimberly F. Sellers, Georgetown University, Washington DC
  • Book:
    The Conway–Maxwell–Poisson Distribution
    Published online:
    02 March 2023
    Print publication:
    09 March 2023, pp 71-123

  • Summary

    The Conway–Maxwell–Poisson distribution has garnered interest in and development of other flexible alternatives to classical distributions. This chapter introduces various distributional extensions and generalities motivated by functions of COM–Poisson random variables, including Conway–Maxwell-inspired generalizations of the Skellam distribution, binomial distribution, negative binomial distribution, the Katz class of distributions, two flexible series system life length distributions, and generalizations of the negative hypergeometric distribution.

  • 7 - COM–Poisson Models for Serially Dependent Count Data

  • Kimberly F. Sellers, Georgetown University, Washington DC
  • Book:
    The Conway–Maxwell–Poisson Distribution
    Published online:
    02 March 2023
    Print publication:
    09 March 2023, pp 251-283

  • Summary

    This chapter considers various models that focus largely on serially dependent variables and the respective methodologies developed with a COM–Poisson underpinning. This chapter first introduces the reader to the various stochastic processes that have been established, including a homogeneous COM–Poisson process, a copula-based COM–Poisson Markov model, and a COM–Poisson hidden Markov model. Meanwhile, there are two approaches for conducting time series analysis on time-dependent count data. One approach assumes that the time dependence occurs with respect to the intensity vector. Under this framework, the usual time series models that assume a continuous variable can be applied. Alternatively, the time series model can be applied directly to the outcomes themselves. Maintaining the discrete nature of the observations, however, requires a different approach referred to as a thinning-based method. Different thinning-based operators can be considered for such models. The chapter then broadens the discussion of dependence to consider COM–Poisson-based spatio-temporal models, thus allowing both for serial and spatial dependence among variables.

  • 11 - Spatial Data

  • from Part IV - Special Types of Data
  • Nils B. Weidmann, Universität Konstanz, Germany
  • Book:
    Data Management for Social Scientists
    Published online:
    03 March 2023
    Print publication:
    09 March 2023, pp 147-165
    • Chapter
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      • Open access
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  • 5 - Managing Data in Spreadsheets

  • from Part II - Data in Files
  • Nils B. Weidmann, Universität Konstanz, Germany
  • Book:
    Data Management for Social Scientists
    Published online:
    03 March 2023
    Print publication:
    09 March 2023, pp 59-73
    • Chapter
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      • Open access
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  • Frontmatter

  • Nils B. Weidmann, Universität Konstanz, Germany
  • Book:
    Data Management for Social Scientists
    Published online:
    03 March 2023
    Print publication:
    09 March 2023, pp i-iv
    • Chapter
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Data Management for Social Scientists
  • From Files to Databases
  • Nils B. Weidmann
  • Published online:
    03 March 2023
    Print publication:
    09 March 2023
  • The 'data revolution' offers many new opportunities for research in the social sciences. Increasingly, social and political interactions can be recorded digitally, leading to vast amounts of new data available for research. This poses new challenges for organizing and processing research data. This comprehensive introduction covers the entire range of data management techniques, from flat files to database management systems. It demonstrates how established techniques and technologies from computer science can be applied in social science projects, drawing on a wide range of different applied examples. This book covers simple tools such as spreadsheets and file-based data storage and processing, as well as more powerful data management software like relational databases. It goes on to address advanced topics such as spatial data, text as data, and network data. This book is one of the first to discuss questions of practical data management specifically for social science projects. This title is also available as Open Access on Cambridge Core.

The Conway–Maxwell–Poisson Distribution
  • Kimberly F. Sellers
  • Published online:
    02 March 2023
    Print publication:
    09 March 2023
  • While the Poisson distribution is a classical statistical model for count data, the distributional model hinges on the constraining property that its mean equal its variance. This text instead introduces the Conway-Maxwell-Poisson distribution and motivates its use in developing flexible statistical methods based on its distributional form. This two-parameter model not only contains the Poisson distribution as a special case but, in its ability to account for data over- or under-dispersion, encompasses both the geometric and Bernoulli distributions. The resulting statistical methods serve in a multitude of ways, from an exploratory data analysis tool, to a flexible modeling impetus for varied statistical methods involving count data. The first comprehensive reference on the subject, this text contains numerous illustrative examples demonstrating R code and output. It is essential reading for academics in statistics and data science, as well as quantitative researchers and data analysts in economics, biostatistics and other applied disciplines.

Page 19 of 118