Results 211 to 220 of about 18,517 (245)
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1996
abstract Up until now, we have been studying how to extract the maximum information from data about models, and how to check their appropriateness, without being concerned about the uses to which the conclusions might be put. In this chapter and the next, we look at various approaches to handling the latter problem.
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abstract Up until now, we have been studying how to extract the maximum information from data about models, and how to check their appropriateness, without being concerned about the uses to which the conclusions might be put. In this chapter and the next, we look at various approaches to handling the latter problem.
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Frequentist Statistical Inference
2011Abstract The relative-frequency view of probability leads to statistical inferences using hypothesis tests and confidence intervals. Parametric tests target inference on distribution parameters, whereas nonparametric tests may relate to any sample statistic of interest.
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2019
This chapter explains some elementary statistical concepts, including the distinction between a statistical estimator computed from data and the parameter that is being estimated. The process of making inferences from data will be discussed, including the importance of accounting for variability in data and one’s uncertainty when making statistical ...
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This chapter explains some elementary statistical concepts, including the distinction between a statistical estimator computed from data and the parameter that is being estimated. The process of making inferences from data will be discussed, including the importance of accounting for variability in data and one’s uncertainty when making statistical ...
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2018
We provide an overview of frequentist model averaging. For point estimation, we consider different methods for selecting the model weights, including those based on AIC, bagging, weighted AIC, stacking and focussed methods. For interval estimation, we consider Wald, MATA and percentile-bootstrap intervals. Use of the methods are illustrated by examples
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We provide an overview of frequentist model averaging. For point estimation, we consider different methods for selecting the model weights, including those based on AIC, bagging, weighted AIC, stacking and focussed methods. For interval estimation, we consider Wald, MATA and percentile-bootstrap intervals. Use of the methods are illustrated by examples
openaire +1 more source