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Titlebook: Probabilistic Causality in Longitudinal Studies; Mervi Eerola Book 1994 Springer-Verlag New York, Inc. 1994 Censoring.Logistic Regression.

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书目名称Probabilistic Causality in Longitudinal Studies
编辑Mervi Eerola
视频video
丛书名称Lecture Notes in Statistics
图书封面Titlebook: Probabilistic Causality in Longitudinal Studies;  Mervi Eerola Book 1994 Springer-Verlag New York, Inc. 1994 Censoring.Logistic Regression.
描述In many applied fields of statistics the concept of causality is central to a scientific investigation. The author‘s aim in this book is to extend the classical theories of probabilistic causality to longitudinal settings and to propose that interesting causal questions can be related to causal effects which can change in time. .The proposed prediction method in this study provides a framework to study the dynamics and the magnitudes of causal effects in a series of dependent events. Its usefulness is demonstrated by the analysis of two examples both drawn from biomedicine, one on bone marrow transplants and one on mental hospitalization. .Consequently, statistical researchers and other scientists concerned with identifying causal relationships will find this an interesting and new approach to this problem.
出版日期Book 1994
关键词Censoring; Logistic Regression; Longitudinal studies; Randomized experiment; innovation; point process; st
版次1
doihttps://doi.org/10.1007/978-1-4612-2684-0
isbn_softcover978-0-387-94367-1
isbn_ebook978-1-4612-2684-0Series ISSN 0930-0325 Series E-ISSN 2197-7186
issn_series 0930-0325
copyrightSpringer-Verlag New York, Inc. 1994
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978-0-387-94367-1Springer-Verlag New York, Inc. 1994
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Book 1994transplants and one on mental hospitalization. .Consequently, statistical researchers and other scientists concerned with identifying causal relationships will find this an interesting and new approach to this problem.
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0930-0325 ne marrow transplants and one on mental hospitalization. .Consequently, statistical researchers and other scientists concerned with identifying causal relationships will find this an interesting and new approach to this problem.978-0-387-94367-1978-1-4612-2684-0Series ISSN 0930-0325 Series E-ISSN 2197-7186
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Concluding Remarks,, in probabilistic causality we would not even require such constant conjunction of events. However, we do not even think that the role of statistical analysis in causality could be confirmative in any sense.
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