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11
Assessment and Propagation of Model Uncertainty
, 1995
"... this paper I discuss a Bayesian approach to solving this problem that has long been available in principle but is only now becoming routinely feasible, by virtue of recent computational advances, and examine its implementation in examples that involve forecasting the price of oil and estimating the ..."
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Cited by 111 (0 self)
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this paper I discuss a Bayesian approach to solving this problem that has long been available in principle but is only now becoming routinely feasible, by virtue of recent computational advances, and examine its implementation in examples that involve forecasting the price of oil and estimating the chance of catastrophic failure of the U.S. Space Shuttle.
A Statistical Perspective on Knowledge Discovery in Databases
, 1996
"... The quest to find models usefully characterizing data is a process central to the scientific method, and has been carried out on many fronts. Researchers from an expanding number of fields have designed algorithms to discover rules or equations that capture key relationships between variables in a d ..."
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Cited by 41 (0 self)
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The quest to find models usefully characterizing data is a process central to the scientific method, and has been carried out on many fronts. Researchers from an expanding number of fields have designed algorithms to discover rules or equations that capture key relationships between variables in a database. The task of this chapter is to provide a perspective on statistical techniques applicable to KDD; accordingly, we review below some major advances in statistics in the last few decades. We next highlight some distinctives of what may be called a "statistical viewpoint." Finally we overview some influential classical and modern statistical methods for practical model induction.
User Interface Affordances in a Planning Representation
 Human Computer Interaction
, 1999
"... This article shows how the concept of affordance in the user interface fits into a wellunderstood artificial intelligence (AI) model of acting in an environment. In this model AI planning research is used to interpret affordances in terms of the costs associated with the generation and execution of ..."
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Cited by 24 (8 self)
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This article shows how the concept of affordance in the user interface fits into a wellunderstood artificial intelligence (AI) model of acting in an environment. In this model AI planning research is used to interpret affordances in terms of the costs associated with the generation and execution of operators in a plan. We motivate our approach with a brief survey of the affordance literature and its connections to the planning literature, and then explore its implications through examples of common user interface mechanisms described in affordance terms. Despite its simplicity, our modeling approach ties together several different threads of practical and theoretical work on affordance into a single conceptual framework. Affordances in a planning representation 3 Contents 1 INTRODUCTION 4 2 PERSPECTIVES ON THE NATURE OF AFFORDANCES 5 3 AFFORDANCES IN PLANNING TERMS 8 4 GENERIC USER INTERFACE AFFORDANCES 13 4.1 Programmable User Models for Affordance Evaluation . . . . . . . . . . ....
The Cost of Adding Parameters to a Model
, 1996
"... For a general regression model with n independent observations we consider the variance of the estimate of a quantity of interest under two scenarios. One scenario is where all the parameters are estimated from the data, the other scenario is where a subset of the parameters are assumed known at the ..."
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Cited by 5 (1 self)
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For a general regression model with n independent observations we consider the variance of the estimate of a quantity of interest under two scenarios. One scenario is where all the parameters are estimated from the data, the other scenario is where a subset of the parameters are assumed known at their true values and the remaining parameters are estimated. We focus on quantities of interest which are defined on the scale of the response variable. We show that, under certain conditions, the ratio of a weighted sum across the design points of the variance of the quantity of interest is given by q=p, where q and p are the number of free parameters in the two scenarios. Thus, in this average sense, the inflation in variance associated with adding parameters, also interpreted as the cost of adding parameters to a model, is directly proportional to the number of parameters. We study models involving power transformations, nonlinear models and exponential family models. Key Words: BoxCox t...
Navigation for Data Analysis Systems
 In Advances in Intelligent Data Analysis: Reasoning about Data
, 1997
"... . Statistical strategies are formal descriptions of the actions and decisions involved in applying statistical tools to a problem. One difficult problem, beyond strategy design issues, is ensuring an effective interaction between the the user and the implementation of the strategy. In this paper ..."
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Cited by 2 (0 self)
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. Statistical strategies are formal descriptions of the actions and decisions involved in applying statistical tools to a problem. One difficult problem, beyond strategy design issues, is ensuring an effective interaction between the the user and the implementation of the strategy. In this paper we review some existing techniques that have been effective in mediating the interaction between users and expert statistical systems for data analysis. We explore the relationship between navigation and statistical decisionmaking and consider features common to both areas. Our analysis leads to the identification of navigation functions that we believe could improve almost any system for statistical data analysis. 1 Introduction Statistical strategies are formal descriptions of the actions and decisions involved in applying statistical tools to a problem [7]. Strategies have been designed and implemented for simple and multiple linear regression [4], MANOVA [7], collinearity diagno...
The Practical Utility of Incorporating Model Selection Uncertainty
, 2004
"... Predictions of disease outcome in prognostic factor models are usually based on one selected model. However, often several models fit the data equally well, but these models might di#er substantially in terms of included explanatory variables and might lead to di#erent predictions for individual pat ..."
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Predictions of disease outcome in prognostic factor models are usually based on one selected model. However, often several models fit the data equally well, but these models might di#er substantially in terms of included explanatory variables and might lead to di#erent predictions for individual patients. For survival data we discuss two approaches for accounting for model selection uncertainty in two data examples with the main emphasis on variable selection in a proportional hazard Cox model. The main aim of our investigation is to establish in which ways either of the two approaches are useful in such prognostic models. The first approach is Bayesian model averaging (BMA) adapted for the proportional hazard model (Volinsky et al., 1997). As a new approach we propose a method which averages over a set of possible models using weights estimated from bootstrap resampling as proposed by Buckland et al. (1997), but in addition we perform an initial screening of variables based on the inclusion frequency of each variable to reduce the set of variables and corresponding models. The main objective of prognostic models is prediction, but the interpretation of single e#ects is also important and models should be general enough to ensure transportability to other clinical centres. In the data examples we compare predictions of the two approaches with "conventional" predictions from one selected model and with predictions from the full model. Confidence intervals are compared in one example. Comparisons are based on the partial predictive score and the Brier score. We conclude that the two model averaging methods yield similar results and are especially useful when there is a high number of potential prognostic factors, most likely some of them without influence in a multivariab...
and
, 905
"... The sensitivity of linear regression coefficients ’ confidence limits to the omission of a confounder ..."
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The sensitivity of linear regression coefficients ’ confidence limits to the omission of a confounder
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, 1996
"... Ihave become indebted to many people in many di erent ways on the long road to a Ph.D. Bill LaRoe, Mike Webb, Charles Chapoton, and Joe O'Rourke helpedmetoget started in graduate school, and I wouldn't have been able to take the rst step without their encouragement and support. Much of the breadth o ..."
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Ihave become indebted to many people in many di erent ways on the long road to a Ph.D. Bill LaRoe, Mike Webb, Charles Chapoton, and Joe O'Rourke helpedmetoget started in graduate school, and I wouldn't have been able to take the rst step without their encouragement and support. Much of the breadth of my dissertation is due to the interest and encouragement of the members of my thesis committee. Mike Sutherland acted as my mentor in the sometimes complex world of statistics. Arny Rosenberg set high standards for intellectual rigor, skill in making complex ideas accessible, and interest in empirical computer science. Victor Lesser's eye for detail is wonderfully balanced by a view of the broad horizons of AI research. The depth of his familiarity with AI made our conversations both exciting and challenging. Paul Cohen, who chaired my committee and advised me through research, shares credit for most of the ideas in this dissertation. When I