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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

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

Document Type:

text

Language:

en

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Metadata may be used without restrictions as long as the oai identifier remains attached to it.

Metadata may be used without restrictions as long as the oai identifier remains attached to it. Minimize

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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

Rights:

Metadata may be used without restrictions as long as the oai identifier remains attached to it.

Metadata may be used without restrictions as long as the oai identifier remains attached to it. Minimize

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Title:

A Note on Teaching Binomial Confidence Intervals

Description:

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

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

Document Type:

text

Language:

en

Rights:

Metadata may be used without restrictions as long as the oai identifier remains attached to it.

Metadata may be used without restrictions as long as the oai identifier remains attached to it. Minimize

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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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Description:

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:

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

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

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:

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

Description:

An algorithm is proposed for determining ################### small-sample confidence intervals for the difference - of two binomial success probabilities based on 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 wit...

An algorithm is proposed for determining ################### small-sample confidence intervals for the difference - of two binomial success probabilities based on 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

Contributors:

The Pennsylvania State University CiteSeerX Archives

Year of Publication:

2010-12-12

Source:

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

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

Document Type:

text

Language:

en

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Title:

Stage-Wise Outlier Detection in Hierarchical Bayesian Repeated Measures Models

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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 prespecified 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 prespecified order. First-stage regression vectors for different 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. As a first diagnostic, we propose the examination, for each subject's curve, of the posterior probability of a first-stage, second-stage, or neither stage outlier relative to the modeling assumptions. These three posterior probabilities are computed for each subject and displayed in a barycentric coordinate plot, a useful device for assessing within-subject and betwe. Minimize

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The Pennsylvania State University CiteSeerX Archives

Year of Publication:

2009-04-13

Source:

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

http://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

DDC:

310 Collections of general statistics *(computed)*

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Title:

Hip Resurfacing Increases Bone Strains Associated with Short-Term Femoral Neck Fracture

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ABSTRACT: Short-term femoral neck fracture is a primary complication associated with contemporary hip resurfacing. Some fractures are associated with neck notching, while others occur in the absence of notching. These unexplained fractures may be due to large magnitude strains near the implant rim, which could cause bone damage accumulation and ...

ABSTRACT: Short-term femoral neck fracture is a primary complication associated with contemporary hip resurfacing. Some fractures are associated with neck notching, while others occur in the absence of notching. These unexplained fractures may be due to large magnitude strains near the implant rim, which could cause bone damage accumulation and eventual neck fracture. We used statistically augmented finite element analysis to identify design and environmental variables that increase bone strains near the implant rim after resurfacing, and lead to strain magnitudes sufficient for rapid damage accumulation. After resurfacing, the compressive strains in the inferior, peripheral neck increased by approximately 25%, particularly when the implant shell was bonded. While the tensile strains in the peripheral neck were low in magnitude in the immediate postoperative models, they increased substantially following compressive damage accumulation. Low bone modulus, within the range of normal bone, and high head load contributed the most to large magnitude strains. Therefore, in some cases, hip resurfacing may causea region of compressivebonedamage todeveloprapidly, which in turn leads to large tensile strainsand potential neck fracture. Our studysuggests that indications for surgeryshouldaccount for bone material quality, and thatrehabilitation protocols shouldavoid Minimize

Contributors:

The Pennsylvania State University CiteSeerX Archives

Year of Publication:

2014-12-03

Source:

http://deepblue.lib.umich.edu/bitstream/handle/2027.42/64126/20884_ftp.pdf?sequence=1

http://deepblue.lib.umich.edu/bitstream/handle/2027.42/64126/20884_ftp.pdf?sequence=1 Minimize

Document Type:

text

Language:

en

Subjects:

hip resurfacing ; finite element analysis ; femoral neck fracture

hip resurfacing ; finite element analysis ; femoral neck fracture Minimize

DDC:

621 Applied physics *(computed)*

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Title:

Subset Selection in Two-Factor Experiments Using Randomization Restricted Designs

Description:

This paper studies subset selection procedures for screening in two-factor treatment designs that employ either a split-plot or strip-plot randomization restricted experimental design laid out in blocks. The goal is to select a subset of treatment combinations associated with the largest mean. In the split-plot design, it is assumed that the blo...

This paper studies subset selection procedures for screening in two-factor treatment designs that employ either a split-plot or strip-plot randomization restricted experimental design laid out in blocks. The goal is to select a subset of treatment combinations associated with the largest mean. In the split-plot design, it is assumed that the block eects, the confounding effects (whole-plot error) and the measurement errors are normally distributed. None of the selection procedures developed depend on the block variances. Subset selection procedures are given for both the case of additive and non-additive factors and for a variety of circumstances concerning the confounding eect and measurement error variances. In particular, procedures are given for (1) known confounding eect and measurement error variances (2) unknown measurement error variance but known confounding eect (3) unknown confounding effect and measurement error variances. The constants required to implement the procedure. Minimize

Contributors:

The Pennsylvania State University CiteSeerX Archives

Year of Publication:

2013-01-25

Source:

http://stat.ohio-state.edu/~tjs//rand-restr.ps

http://stat.ohio-state.edu/~tjs//rand-restr.ps Minimize

Document Type:

text

Language:

en

Subjects:

ranking and selection ; split-plot design ; strip-plot design ; two-way layout ; optimal design ; least favorable con guration

ranking and selection ; split-plot design ; strip-plot design ; two-way layout ; optimal design ; least favorable con guration Minimize

DDC:

310 Collections of general statistics *(computed)*

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

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

Contributors:

The Pennsylvania State University CiteSeerX Archives

Year of Publication:

2013-08-20

Source:

http://www.stat.osu.edu/~amd/papers/BKA-final.pdf

http://www.stat.osu.edu/~amd/papers/BKA-final.pdf Minimize

Document Type:

text

Language:

en

Subjects:

Some key words ; Control treatment ; Dunnett’s critical values ; Expected average allowance ; Expected maximum allowance ; Multiple comparisons ; One-way layout ; Optimal design

Some key words ; Control treatment ; Dunnett’s critical values ; Expected average allowance ; Expected maximum allowance ; Multiple comparisons ; One-way layout ; Optimal design Minimize

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