Scott Zeger, Peter Diggle, and Kung-Yee Liang
- Published in print:
- 2005
- Published Online:
- September 2007
- ISBN:
- 9780198566540
- eISBN:
- 9780191718038
- Item type:
- chapter
- Publisher:
- Oxford University Press
- DOI:
- 10.1093/acprof:oso/9780198566540.003.0009
- Subject:
- Mathematics, Probability / Statistics
This chapter reviews the biomedical and public health developments that will influence biostatistical research and practice in the near future, such as advances in molecular biology, and measuring ...
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This chapter reviews the biomedical and public health developments that will influence biostatistical research and practice in the near future, such as advances in molecular biology, and measuring DNA sequences and gene and protein expression levels. It is argued that the success of biostatistics will derive largely from a model-based approach, which uses and applies the principle of conditioning. Statistical models and inferences that are central to this model-based approach are described and contrasted with computationally-intensive strategies and a design-based approach. Increasingly complex models, different sources of uncertainty, and clustered observational units are viewed as future challenges for the model-based approach. Causal inference and statistical computing are discussed as topics believed to be central to biostatistics in the near future.Less
This chapter reviews the biomedical and public health developments that will influence biostatistical research and practice in the near future, such as advances in molecular biology, and measuring DNA sequences and gene and protein expression levels. It is argued that the success of biostatistics will derive largely from a model-based approach, which uses and applies the principle of conditioning. Statistical models and inferences that are central to this model-based approach are described and contrasted with computationally-intensive strategies and a design-based approach. Increasingly complex models, different sources of uncertainty, and clustered observational units are viewed as future challenges for the model-based approach. Causal inference and statistical computing are discussed as topics believed to be central to biostatistics in the near future.
A. C. Davison, Yadolah Dodge, and N. Wermuth (eds)
- Published in print:
- 2005
- Published Online:
- September 2007
- ISBN:
- 9780198566540
- eISBN:
- 9780191718038
- Item type:
- book
- Publisher:
- Oxford University Press
- DOI:
- 10.1093/acprof:oso/9780198566540.001.0001
- Subject:
- Mathematics, Probability / Statistics
Sir David Cox is among the most important statisticians of the past half-century, making pioneering and highly influential contributions to a wide range of topics in statistics and applied ...
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Sir David Cox is among the most important statisticians of the past half-century, making pioneering and highly influential contributions to a wide range of topics in statistics and applied probability. This book contains summaries of the invited talks at a meeting held at the University of Neuchâtel in July 2004 to celebrate David Cox’s 80th birthday. The chapters describe current developments across a wide range of topics, ranging from statistical theory and methods, through applied probability and modelling, to applications in areas including finance, epidemiology, hydrology, medicine, and social science. The book contains chapters by numerous well-known statisticians. It provides a summary of current thinking across a wide front by leading statistical thinkers.Less
Sir David Cox is among the most important statisticians of the past half-century, making pioneering and highly influential contributions to a wide range of topics in statistics and applied probability. This book contains summaries of the invited talks at a meeting held at the University of Neuchâtel in July 2004 to celebrate David Cox’s 80th birthday. The chapters describe current developments across a wide range of topics, ranging from statistical theory and methods, through applied probability and modelling, to applications in areas including finance, epidemiology, hydrology, medicine, and social science. The book contains chapters by numerous well-known statisticians. It provides a summary of current thinking across a wide front by leading statistical thinkers.
Andrew Gelman and Deborah Nolan
- Published in print:
- 2017
- Published Online:
- September 2017
- ISBN:
- 9780198785699
- eISBN:
- 9780191827518
- Item type:
- chapter
- Publisher:
- Oxford University Press
- DOI:
- 10.1093/oso/9780198785699.003.0021
- Subject:
- Mathematics, Educational Mathematics
In this chapter, we describe the philosophy, goals, syllabus, and activities for a course that we have developed in data science course. In this course we integrate topics from computing, statistics, ...
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In this chapter, we describe the philosophy, goals, syllabus, and activities for a course that we have developed in data science course. In this course we integrate topics from computing, statistics, and working with data. This integrated approach addresses many core aspects in statistics training, including statistical thinking, the role of context in addressing a statistical problem, statistical communication through code, and the balance between programming and mathematical approaches to problems. When designing this course, we asked ourselves what our students ought to be able to do computationally. While we do provide a list of technical material, we also considered the broader goals of the course. Examples include plotting on Google Earth and developing a spam filter for unwanted email.Less
In this chapter, we describe the philosophy, goals, syllabus, and activities for a course that we have developed in data science course. In this course we integrate topics from computing, statistics, and working with data. This integrated approach addresses many core aspects in statistics training, including statistical thinking, the role of context in addressing a statistical problem, statistical communication through code, and the balance between programming and mathematical approaches to problems. When designing this course, we asked ourselves what our students ought to be able to do computationally. While we do provide a list of technical material, we also considered the broader goals of the course. Examples include plotting on Google Earth and developing a spam filter for unwanted email.
