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Title:

A Note on Teaching Binomial Confidence Intervals

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This paper addresses this same important question of which binomial confidence interval method should be the standard method taught in elementary courses. We argue that a third, easily motivated, variant of the z-interval should be the standard asymptotic method presented in elementary books. We also recommend that an alternative method be simul...

This paper addresses this same important question of which binomial confidence interval method should be the standard method taught in elementary courses. We argue that a third, easily motivated, variant of the z-interval should be the standard asymptotic method presented in elementary books. We also recommend that an alternative method be simultaneously presented for use in small sample applications; this method produces intervals that Minimize

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The Pennsylvania State University CiteSeerX Archives

Year of Publication:

2009-04-13

Source:

ftp://ftp.stat.uni-muenchen.de/pub/sfb386/paper87.ps.Z

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text

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en

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Title:

Invariant Small Sample Confidence Intervals For The Difference Of Two Success Probabilities

Description:

An algorithm is proposed for determining 9999 small-sample confidence intervals for the difference - of two binomial success probabilities based on and trials, respectively. The interval covers the true with probability at least for all ! ; it is invariant with respect to relabeling of the two populations and with respect to interchanging the ou...

An algorithm is proposed for determining 9999 small-sample confidence intervals for the difference - of two binomial success probabilities based on and trials, respectively. The interval covers the true with probability at least for all ! ; it is invariant with respect to relabeling of the two populations and with respect to interchanging the outcomes of success and failure intervals. Coverage and expected length comparisons are made with the small sample "#$ # tail intervals of Santner and Snell (1980). A FORTRAN program implementing the algorithm is available from the authors. Minimize

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The Pennsylvania State University CiteSeerX Archives

Year of Publication:

2010-12-12

Source:

http://stat.ohio-state.edu/~tjs/TJS-SY//tjs-sy.pdf

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Document Type:

text

Language:

en

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Title:

A Note on Teaching Large-Sample Binomial Confidence Intervals

Description:

This paper addresses the question of which large--sample binomial confidence interval method to teach in elementary courses. Recently, Goodall (1995), Simon (1996), and the references therein, have discussed the merits of several large--sample systems of binomial intervals. Three of the systems considered these articles are the

This paper addresses the question of which large--sample binomial confidence interval method to teach in elementary courses. Recently, Goodall (1995), Simon (1996), and the references therein, have discussed the merits of several large--sample systems of binomial intervals. Three of the systems considered these articles are the Minimize

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The Pennsylvania State University CiteSeerX Archives

Year of Publication:

2009-04-14

Source:

http://stat.ohio-state.edu/~tjs/teaching-stats.pdf

http://stat.ohio-state.edu/~tjs/teaching-stats.pdf Minimize

Document Type:

text

Language:

en

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Title:

Efficient designs for one-sided comparisons of two or three treatments with a control in a one-way layout

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The problem of providing lower confidence bounds for the mean improvements of p >= 2 test treatments over a control treatment is considered. The expected average and expected maximum allowances are two criteria for comparing different systems of confidence intervals or bounds. In this paper, lower bounds are derived for the expected average allo...

The problem of providing lower confidence bounds for the mean improvements of p >= 2 test treatments over a control treatment is considered. The expected average and expected maximum allowances are two criteria for comparing different systems of confidence intervals or bounds. In this paper, lower bounds are derived for the expected average allowance and the expected maximum allowance of Dunnett's simultaneous lower confidence bounds for the p mean improvements. These lower bounds hold for any p >= 2 and any allocation of sample sizes. For p = 2 test treatments, sample allocations are given for which the bounds are achievable. For p = 3 test treatments, a tighter set of bounds is derived which enables easy determination of the sample allocation required to achieve highly efficient designs. A table of the bounds for the expected average and expected maximum allowances and the sample allocation that achieves these bounds is given for p = 2, 3. The theoretical results can easily be adapted to cover upper confidence bounds. Copyright 2006, Oxford University Press. Minimize

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article

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Title:

Detecting Stage-Wise Outliers in Hierarchical Bayesian Linear Models of Repeated Measures Data

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Description:

for multi-stage models. We propose numerical and graphical methods for outlier detection in hierarchical Bayes modeling and analyses of repeated measures regression data from multiple subjects; data from a single subject are generically called a “curve. ” The first-stage of our model has curve-specific regression coeffi-cients with possibly auto...

for multi-stage models. We propose numerical and graphical methods for outlier detection in hierarchical Bayes modeling and analyses of repeated measures regression data from multiple subjects; data from a single subject are generically called a “curve. ” The first-stage of our model has curve-specific regression coeffi-cients with possibly autoregressive errors of a prespecified order. The first-stage regression vectors for different curves are linked in a second-stage modeling step, possibly involving additional regression variables. Detection of the stage at which the curve appears to be an outlier and the magnitude and specific component of the violation at that stage is accomplished by embedding the null model into a larger parametric model that can accommodate such unusual observations. As a first diagnostic, we examine the posterior probabilities of first-stage and second-stage anoma-lies relative to the modeling assumptions for each curve. For curves where there is evidence of a model violation at either stage, we propose additional numerical and graphical diagnostics. For first-stage violations, the diagnostics identify the specific measurements within a curve that are anomalous. For second-stage violations, the diagnostics identify the curve parameters that are unusual relative to the pattern of parameter values for the majority of the curves. We give two examples to illustrate the diagnostics, develop a BUGS program to compute them using MCMC techniques, and examine the sensitivity of the conclusions to the prior modeling assumptions. 2 1 Minimize

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The Pennsylvania State University CiteSeerX Archives

Year of Publication:

2008-07-01

Source:

http://www.stat.ohio-state.edu/~tjs/papers/peruggia-tjs-ho.pdf

http://www.stat.ohio-state.edu/~tjs/papers/peruggia-tjs-ho.pdf Minimize

Document Type:

text

Language:

en

Subjects:

Running Title ; Hierarchical Bayes Outlier Detection Keywords ; Autoregressive errors ; BUGS ; Graphical diagnostics ; Model-based diagnostics ; Outlier accommodation models ; Diagnostics

Running Title ; Hierarchical Bayes Outlier Detection Keywords ; Autoregressive errors ; BUGS ; Graphical diagnostics ; Model-based diagnostics ; Outlier accommodation models ; Diagnostics Minimize

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310 Collections of general statistics *(computed)*

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Title:

Theory of screening procedures to identify robust product designs using fractional factorial experiments

Description:

In many quality improvement experiments, there are one or more “control factors ” that can be used to determine a final product design (or manufacturing process), and one or more “environmental factors ” that vary under field (or manufacturing) conditions. This paper considers applications where the product design or process design is considered...

In many quality improvement experiments, there are one or more “control factors ” that can be used to determine a final product design (or manufacturing process), and one or more “environmental factors ” that vary under field (or manufacturing) conditions. This paper considers applications where the product design or process design is considered to be seriously flawed if its performance is inferior at any level of the environmental factor; for example, if a particular prosthetic heart valve design has poor fluid flow characteristics at certain pulse rates, then a manufacturer will not want to put this design into production. In such a situation, the appropriate measure of a product’s quality is its worst performance over the levels of the environmental factor. This paper develops theory for a class of subset selection procedures that identify product designs which maximize the worst-case performance over the environmental conditions for general Combined array experiments. Minimize

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The Pennsylvania State University CiteSeerX Archives

Year of Publication:

2009-08-06

Source:

http://www.stat.ohio-state.edu/~tjs/PAN/JSPI-2003/tech-rpt.pdf

http://www.stat.ohio-state.edu/~tjs/PAN/JSPI-2003/tech-rpt.pdf Minimize

Document Type:

text

Language:

en

Subjects:

Combined-array ; Inner array ; Minimax approach ; Outer array ; Productarray ; Quality improvement ; Response model ; Robust process design ; Screening ; Simulation ; Subset selection ; Variance reduction

Combined-array ; Inner array ; Minimax approach ; Outer array ; Productarray ; Quality improvement ; Response model ; Robust process design ; Screening ; Simulation ; Subset selection ; Variance reduction Minimize

DDC:

670 Manufacturing *(computed)*

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Title:

Screening Procedures to Identify Robust Product or Process Designs Using Fractional Factorial Experiments

Description:

In many quality improvement experiments, there are one or more "control" factors that can be modified to determine a final product design or manufacturing process, and one or more "environmental" (or " noise") factors that vary under field or manufacturing conditions. In many applications, the product design or process design is considered serio...

In many quality improvement experiments, there are one or more "control" factors that can be modified to determine a final product design or manufacturing process, and one or more "environmental" (or " noise") factors that vary under field or manufacturing conditions. In many applications, the product design or process design is considered seriously flawed if its performance is poor for any level of the environmental factor. For example, if a particular prosthetic heart valve design has poor fluid flow characteristics for certain flow rates, then a manufacturer will not want to put this design into production. Thus this paper considers cases when it is appropriate to measure a product's quality to be its worst performance over the levels of the environmental factor. We consider the frequently occurring case of combined-array experiments and extend the subset selection methodology of Gupta (1956, 1965) to provide statistical screening procedures to identify product designs that maximize. Minimize

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The Pennsylvania State University CiteSeerX Archives

Year of Publication:

2009-04-13

Source:

ftp://ftp.stat.uni-muenchen.de/pub/sfb386/paper92.ps.Z

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Document Type:

text

Language:

en

Subjects:

Combined-array ; Inner array ; Minimax approach ; Outer array ; Productarray ; Quality improvement ; Response model ; Screening ; Simulation ; Subset selection ; Variance reduction

Combined-array ; Inner array ; Minimax approach ; Outer array ; Productarray ; Quality improvement ; Response model ; Screening ; Simulation ; Subset selection ; Variance reduction Minimize

DDC:

670 Manufacturing *(computed)*

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Title:

Stage-Wise Outlier Detection in Hierarchical Bayesian Repeated Measures Models

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Description:

We propose numerical and graphical methods for outlier detection in hierarchical Bayes analyses of repeated measures regression data. We consider a model that allows observations on the same subject (typically a curve of measurements taken at equidistant time points or locations) to have autoregressive errors of a prespeci ed order. First-stage ...

We propose numerical and graphical methods for outlier detection in hierarchical Bayes analyses of repeated measures regression data. We consider a model that allows observations on the same subject (typically a curve of measurements taken at equidistant time points or locations) to have autoregressive errors of a prespeci ed order. First-stage regression vectors for dierent subjects are \tied together" in a second-stage modeling step, possibly involving additional regression variables. Outlier detection is accomplished by embedding the null model into a larger parametric model that can accommodate unusual observations. Minimize

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The Pennsylvania State University CiteSeerX Archives

Year of Publication:

2009-04-18

Source:

http://www.stat.ohio-state.edu/~peruggia/papers/ho.ps

http://www.stat.ohio-state.edu/~peruggia/papers/ho.ps Minimize

Document Type:

text

Language:

en

Subjects:

Autoregressive errors ; Gibbs sampler ; Graphical diagnostics ; Model-based diagnostics

Autoregressive errors ; Gibbs sampler ; Graphical diagnostics ; Model-based diagnostics Minimize

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Title:

The Planning and Analysis of Industrial Selection And Screening Experiments

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The Pennsylvania State University CiteSeerX Archives

Year of Publication:

2012-04-13

Source:

http://www.stat.ohio-state.edu/~tjs//handbook111700.pdf

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text

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en

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Title:

Selection and Screening Procedures to Determine Optimal Product Designs

Description:

To compare several promising product designs, manufacturers must measure their performance under multiple environmental conditions. In many applications, a product design is considered to be seriously flawed if its performance is poor for any level of the environmental factor. For example, if a particular automobile battery design does not funct...

To compare several promising product designs, manufacturers must measure their performance under multiple environmental conditions. In many applications, a product design is considered to be seriously flawed if its performance is poor for any level of the environmental factor. For example, if a particular automobile battery design does not function well under temperature extremes, then a manufacturer may not want to put this design into production. Thus this paper considers the measure of a product's quality to be its worst performance over the levels of the environmental factor. We develop statistical procedures to identify (a near) optimal product design among a given set of product designs, i.e., the manufacturing design that maximizes the worst product performance over the levels of the environmental variable. We accomplish this for intuitive procedures based on the split-plot experimental design (and the randomized complete block design as a special case); split-plot designs have . Minimize

Contributors:

The Pennsylvania State University CiteSeerX Archives

Year of Publication:

2009-04-13

Source:

ftp://ftp.stat.uni-muenchen.de/pub/sfb386/paper52.ps.Z

ftp://ftp.stat.uni-muenchen.de/pub/sfb386/paper52.ps.Z Minimize

Document Type:

text

Language:

en

DDC:

670 Manufacturing *(computed)*

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