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Modern mathematical statistics with applications
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ISBN: 0495118605 9780495118602 Year: 2007 Publisher: Belmont (Calif.) : Thomson, Brooks/Cole,

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Mathematics and statistics for science
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ISBN: 3031053176 3031053184 Year: 2022 Publisher: Cham, Switzerland : Springer,

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Proceedings of the 2022 International Conference on Mathematical Statistics and Economic Analysis (MSEA 2022).
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ISBN: 9464630426 9464630418 Year: 2023 Publisher: Dordrecht : Atlantis Press (Zeger Karssen),

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This is an open access book. 2022 International Conference on Mathematical Statistics and Economic Analysis(MSEA 2022) will be held in Dalian, China from May 27 to 29, 2022. Based on probability theory, mathematical statistics studies the statistical regularity of a large number of random phenomena, and infers and forecasts the whole. Economic development is very important to people's life and the country. Through data statistics and analysis, we can quickly understand the law of economic development. This conference combines mathematical statistics and economic analysis for the first time to explore the relationship between them, so as to provide a platform for experts and scholars in the field of mathematical statistics and economic analysis to exchange and discuss.


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Applying quantitative bias analysis to epidemiologic data
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ISBN: 3030826724 3030826732 Year: 2022 Publisher: Cham, Switzerland : Springer,

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Applied statistics and data science : proceedings of Statistics 2021 Canada, selected contributions
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ISBN: 3030861325 3030861333 Year: 2021 Publisher: Cham, Switzerland : Springer,

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Applied statistical methods : ISGES 2020, Pune, India, January 2-4
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ISBN: 9811679312 9811679320 Year: 2022 Publisher: Gateway East, Singapore : Springer,

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Handbook of statistical bioinformatics
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ISBN: 3662659018 3662659026 Year: 2022 Publisher: Berlin : Springer,

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Statistical causal discovery : LiNGAM approach
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ISBN: 9784431557845 9784431557838 9784431557852 Year: 2022 Publisher: Tokyo, Japan : Springer,

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This is the first book to provide a comprehensive introduction to a new semiparametric causal discovery approach known as LiNGAM, with the fundamental background needed to understand it. It offers a general overview of the basics of the LiNGAM approach for causal discovery, estimation principles, and algorithms. This semiparametric approach is one of the most exciting new topics in the field of causal discovery. The new framework assumes parametric assumptions on the functional forms of structural equations but makes no assumption on the distributions of exogenous variables other than non-Gaussianity. It provides data-analysis tools capable of estimating a much wider class of causal relations even in the presence of hidden common causes. This feature is in contrast to conventional nonparametric approaches based on conditional independence of variables. This book is highly recommended to readers who seek an in-depth and up-to-date overview of this new causal discovery approach to advance the technique as well as to those who are interested in applying this approach to real-world problems. This LiNGAM approach should become a standard item in the toolbox of statisticians, machine learners, and practitioners who need to perform observational studies.


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Engineering statistics : an introduction
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ISBN: 9783031050107 9783031050091 9783031050114 9783031050121 Year: 2022 Publisher: Cham, Switzerland : Springer,

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This book presents a concise and focused introduction to engineering statistics, emphasizing topics and concepts that a practicing engineer is mostly likely to use: the display of data, confidence intervals, hypothesis testing, fitting straight lines to data, and designing experiments to find the impact of process changes on a system or its output. It introduces the language of statistics, derives equations with sufficient detail so that there is no mystery as to how they came about, makes extensive use of tables to collect and summarize important formulas and concepts, and utilizes enhanced graphics that are packed with visual information to illustrate the meaning of the equations and their usage.


Book
Visualizing linear models
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ISBN: 3030641678 303064166X Year: 2021 Publisher: Cham, Switzerland : Springer,

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