关于数据分析统计方法的要求(概括性文章)

A. I. Orlov
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引用次数: 0

摘要

半个世纪以来,在向不同专业的研究人员提供咨询、审阅文章和书籍、反对学位论文等活动中,我们了解了数以百计的关于统计方法的发展和应用的具体研究。在进行研究和发表研究成果时发现了各种不足之处,这些不足之处妨碍了对所获数据的理解,在某些情况下还使人对结论的充分性产生怀疑。因此,对数据处理方法和统计数据分析结果的呈现方式提出自然要求似乎是可取的。本研究旨在对此类要求的若干表述方式进行初步探讨。我们从现代应用统计范式出发,以非参数和非数字统计为基础,取代 19 世纪的原始范式和 20 世纪中叶使用参数分布系统的范式。在描述和讨论统计数据的分析程序时,有必要从生成所研究数据的概率统计模型入手。必须从测量理论出发,根据测量理论,数据分析的第一步是确定测量的尺度。统计推论必须在数据测量尺度的允许变换下保持不变。由于几乎所有真实数据的分布都是非正态分布,因此应优先考虑非参数公式。使用参数系列分布的可能性必须经过仔细论证。根据统计假设检验理论,必须明确零假设和备择假设。有必要研究从模型中得出的结论在初始数据和模型假设发生可接受的变化时的稳定性。作者的其他一些著作将专门讨论统计模型和方法要求系统的开发问题。
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On the requirements for statistical methods of data analysis (generalizing article)
Half a century activity in consulting researchers of various specialties, reviewing articles and books, opposing dissertations provided the possibility of getting acquainted with hundreds of specific studies on the development and application of statistical methods. A variety of shortcomings in conducting research and publishing the results of studies have been revealed, which hinder the perception of the data obtained, and in some cases cast doubt on the adequacy of the conclusions. Therefore, it appeared advisable to develop natural requirements for methods of data processing and presentation of the results of statistical data analysis. This study is devoted to an initial consideration of a number of formulations of such requirements. We proceed from the modern paradigm of applied statistics, based on non-parametric and non-numerical statistics which replace the primitive paradigm of the 19th century and the paradigm of the middle of the 20th century using parametric distribution systems. When describing and discussing the procedures for analyzing statistical data, it is necessary to start with probabilistic-statistical models for generating the data under study. It is necessary to proceed from the theory of measurements, according to which the first step in the data analysis is identification of the scales in which they are measured. Statistical inference must be invariant under the allowable transformations of data measurement scales. Since almost all distributions of real data are non-normal, a preference should be given to non-parametric formulations. The possibility of using parametric families of distributions must be carefully justified. In accordance with the theory of testing statistical hypothesis, both the null and alternative hypotheses must be specified. It is necessary to study the stability of the conclusions drawn from the model with respect to acceptable changes in the initial data and assumptions of the model. A number of further publications of the author will be devoted to the problems of developing a system of requirements for statistical models and methods.
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