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Confidence Intervals
"... • We have discussed point estimates: – as an estimate of a success probability, – as an estimate of population mean, • These point estimates are almost never exactly equal to ..."
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• We have discussed point estimates: – as an estimate of a success probability, – as an estimate of population mean, • These point estimates are almost never exactly equal to
Using confidence intervals in withinsubject designs
 PSYCHONOMIC BULLETIN & REVIEW
, 1994
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Confidence Intervals
"... Diseases like chronic depression, schizophrenia, substance abuse, HIV infection, epilepsy,... are treated in multiple stages – At each stage, treatment is adapted to the available information on patient characteristics and past treatments. Information collected on patient characteristics, treatments ..."
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Diseases like chronic depression, schizophrenia, substance abuse, HIV infection, epilepsy,... are treated in multiple stages – At each stage, treatment is adapted to the available information on patient characteristics and past treatments. Information collected on patient characteristics, treatments administered, and patient outcomes together form a (short) finite horizon trajectory. Goal: To learn a “good ” treatment policy from a training dataset (batch) of finite horizon trajectories. 3 / 17 Multistage Medical Decision Making Somewhat distinct from the standard Reinforcement Learning setup! Poorly understood system dynamics (e.g., no physical laws) Possibly highdimensional state space, but finite action space (action = choice of treatment) NonMarkovian state transitions Very short horizon (say, 2 − 4 stages) Limited amount of data – They come from expensive and timeconsuming clinical trials. We consider fitted Qiteration (e.g., Antos et al., NIPS 2007) with linear function approximation in this context. 4 / 17
Confidence intervals
, 2008
"... Projectionbased methods of inference on subsets of parameters are useful for obtaining tests that do not overreject the true parameter values. However, they are also often criticized for being conservative. We show that the usual method of projection can be modified to obtain tests that are as pow ..."
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Projectionbased methods of inference on subsets of parameters are useful for obtaining tests that do not overreject the true parameter values. However, they are also often criticized for being conservative. We show that the usual method of projection can be modified to obtain tests that are as powerful as the conventional tests for subsets of parameters. Like the usual projectionbased methods, one can always put an upper bound to the rate at which the new method overrejects the true value of the parameters of interest. The new method is described in the context of GMM with possibly weakly identified parameters. JEL Classification: C12; C13; C30
Asymptotic Confidence Intervals for Indirect Effects in Structural EQUATION MODELS
 IN SOCIOLOGICAL METHODOLOGY
, 1982
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Credibility of confidence intervals
 Prepared for Conference on Advanced Statistical Techniques in Particle Physics
, 2002
"... Classical confidence intervals are often misunderstood by particle physicists and the general public alike. The confusion arises from the two different definitions of probability in common use. As a result, there is general dissatisfaction when confidence intervals are empty or they exclude paramete ..."
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Cited by 3 (0 self)
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Classical confidence intervals are often misunderstood by particle physicists and the general public alike. The confusion arises from the two different definitions of probability in common use. As a result, there is general dissatisfaction when confidence intervals are empty or they exclude
Comparison to Bayesian Confidence Intervals
, 2003
"... We construct bootstrap confidence intervals for smoothing spline and smoothing spline ANOVA estimates based on Gaussian data, and penalized likelihood smoothing spline estimates based on data from exponential families. Several variations of bootstrap confidence intervals are considered and compared. ..."
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We construct bootstrap confidence intervals for smoothing spline and smoothing spline ANOVA estimates based on Gaussian data, and penalized likelihood smoothing spline estimates based on data from exponential families. Several variations of bootstrap confidence intervals are considered and compared
Generalized confidence intervals
 J Am Stat Assoc
, 1993
"... The definition of a confidence interval is generalized so that problems such as constructing exact confidence regions for the difference in two normal means can be tackled without the assumption of equal variances. Under certain conditions, the extended definition is shown to preserve a repeated sam ..."
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Cited by 45 (0 self)
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The definition of a confidence interval is generalized so that problems such as constructing exact confidence regions for the difference in two normal means can be tackled without the assumption of equal variances. Under certain conditions, the extended definition is shown to preserve a repeated
Strong confidence intervals for autoregression
, 2008
"... In this short preliminary note I apply the methodology of gametheoretic probability to calculating nonasymptotic confidence intervals for the coefficient of a simple first order scalar autoregressive model. The most distinctive feature of the proposed procedure is that with high probability it prod ..."
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In this short preliminary note I apply the methodology of gametheoretic probability to calculating nonasymptotic confidence intervals for the coefficient of a simple first order scalar autoregressive model. The most distinctive feature of the proposed procedure is that with high probability
Lecture 5: Confidence Intervals
"... Confidence intervals (or regions) are an important and often underused area of statistical inference, previously defined as the science of using a data sample to deduce various population attributes. As was true for our presentation of estimators in Lecture 5, the interpretation of confidence interv ..."
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Confidence intervals (or regions) are an important and often underused area of statistical inference, previously defined as the science of using a data sample to deduce various population attributes. As was true for our presentation of estimators in Lecture 5, the interpretation of confidence
Results 1  10
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662,444