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Title:

Simultaneous estimation of quantile curves using quantile sheets

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The results of quantile smoothing often show crossing curves, in particular, for small data sets. We define a surface, called a quantile sheet, on the domain of the independent variable and the probability. Any desired quantile curve is obtained by evaluating the sheet for a fixed probability. This sheet is modeled by $$P$$-splines in form of te...

The results of quantile smoothing often show crossing curves, in particular, for small data sets. We define a surface, called a quantile sheet, on the domain of the independent variable and the probability. Any desired quantile curve is obtained by evaluating the sheet for a fixed probability. This sheet is modeled by $$P$$-splines in form of tensor products of $$B$$-splines with difference penalties on the array of coefficients. The amount of smoothing is optimized by cross-validation. An application for reference growth curves for children is presented. Minimize

Publisher:

Springer-Verlag

Year of Publication:

2013-01-01

Source:

AStA Advances in Statistical Analysis, 2013-01-01, Volume 97, pp 77-87

AStA Advances in Statistical Analysis, 2013-01-01, Volume 97, pp 77-87 Minimize

Document Type:

Original Paper

Language:

En

Subjects:

$$P$$-splines ; Quantiles ; Smoothing ; Tensor product

$$P$$-splines ; Quantiles ; Smoothing ; Tensor product Minimize

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Title:

Penalized regression with individual deviance effects

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The present work addresses the problem of model estimation and computations for discrete data when some covariates are modeled smoothly using splines. We propose to introduce and explicitly estimate individual deviance effects (one for each observation), constrained by a ridge penalty. This turns out to be an effective way to absorb model excess...

The present work addresses the problem of model estimation and computations for discrete data when some covariates are modeled smoothly using splines. We propose to introduce and explicitly estimate individual deviance effects (one for each observation), constrained by a ridge penalty. This turns out to be an effective way to absorb model excess variation and detect systematic patterns. Large but very sparse systems of penalized likelihood equations have to be solved. We present fast and compact algorithms for fitting, estimation and computation of the effective dimension. Applications to counts, binomial, and survival data illustrate practical use of this model. Minimize

Publisher:

Springer-Verlag

Year of Publication:

2010-06-01

Source:

Computational Statistics, 2010-06-01, Volume 25, pp 341-361

Computational Statistics, 2010-06-01, Volume 25, pp 341-361 Minimize

Document Type:

Original Paper

Language:

En

Subjects:

Generalized linear models ; Smoothing ; Effective dimension ; Penalized regression

Generalized linear models ; Smoothing ; Effective dimension ; Penalized regression Minimize

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Title:

production using expectile frontier zones

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Demographic Research a free, expedited, online journal of peer-reviewed research and commentary in the population sciences published by the

Demographic Research a free, expedited, online journal of peer-reviewed research and commentary in the population sciences published by the Minimize

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The Pennsylvania State University CiteSeerX Archives

Year of Publication:

2013-09-25

Source:

http://www.demographic-research.org/volumes/vol21/5/21-5.pdf

http://www.demographic-research.org/volumes/vol21/5/21-5.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:

Growth charts for children with Ellis–van Creveld syndrome

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Ellis–van Creveld (EvC) syndrome is a congenital malformation syndrome with marked growth retardation. In this study, specific growth charts for EvC patients were derived to allow better follow-up of growth and earlier detection of growth patterns unusual for EvC. With the use of 235 observations of 101 EvC patients (49 males, 52 females), growt...

Ellis–van Creveld (EvC) syndrome is a congenital malformation syndrome with marked growth retardation. In this study, specific growth charts for EvC patients were derived to allow better follow-up of growth and earlier detection of growth patterns unusual for EvC. With the use of 235 observations of 101 EvC patients (49 males, 52 females), growth charts for males and females from 0 to 20 years of age were derived. Longitudinal and cross-sectional data were collected from an earlier review of growth data in EvC, a database of EvC patients, and from recent literature. To model the growth charts, the GAMLSS package for the R statistical program was used. Height of EvC patients was compared to healthy children using Dutch growth charts. Data are presented both on a scale for age and on a scale for the square root of age. Compared to healthy Dutch children, mean height standard deviation score values for male and female EvC patients were −3.1 and −3.0, respectively. The present growth charts should be useful in the follow-up of EvC patients. Most importantly, early detection of growth hormone deficiency, known to occur in EvC, will be facilitated. Minimize

Publisher:

Springer-Verlag

Year of Publication:

2011-02-01

Source:

European Journal of Pediatrics, 2011-02-01, Volume 170, pp 207-211

European Journal of Pediatrics, 2011-02-01, Volume 170, pp 207-211 Minimize

Document Type:

Original Paper

Language:

En

Subjects:

Growth ; Body height ; Ellis–van Creveld syndrome ; Growth charts

Growth ; Body height ; Ellis–van Creveld syndrome ; Growth charts Minimize

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Title:

Splines, Knots, and Penalties

Description:

Penalized splines have gained much popularity as a flexible tool for smoothing and semi-parametric models. Two approaches have been advocated: 1) use a B-spline basis, equally-spaced knots and difference penalties (Eilers and Marx, 1996) and 2) use truncated power functions, knots based on quantiles of the independent variable and a ridge penalt...

Penalized splines have gained much popularity as a flexible tool for smoothing and semi-parametric models. Two approaches have been advocated: 1) use a B-spline basis, equally-spaced knots and difference penalties (Eilers and Marx, 1996) and 2) use truncated power functions, knots based on quantiles of the independent variable and a ridge penalty (Ruppert, Wand and Carroll, 2003). We compare the two approaches on many aspects: numerical stability, quality of the fit, interpolation/extrapolation, derivative estimation, visual presentation and extension to multi-dimensional smoothing. We discuss mixed model and Bayesian parallels to penalized regression. We conclude that B-splines with difference penalties are clearly to be preferred. Keywords: P-splines, truncated power functions, interpolation, smoothing. 1 1 Minimize

Contributors:

The Pennsylvania State University CiteSeerX Archives

Year of Publication:

2008-12-30

Source:

http://www.stat.lsu.edu/faculty/marx/splines_knots_penalties.pdf

http://www.stat.lsu.edu/faculty/marx/splines_knots_penalties.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:

Haplotype Estimation from Fuzzy Genotypes Using Penalized Likelihood

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The Composite Link Model is a generalization of the generalized linear model in which expected values of observed counts are constructed as a sum of generalized linear components. When combined with penalized likelihood, it provides a powerful and elegant way to estimate haplotype probabilities from observed genotypes. Uncertain (‘‘fuzzy’’) geno...

The Composite Link Model is a generalization of the generalized linear model in which expected values of observed counts are constructed as a sum of generalized linear components. When combined with penalized likelihood, it provides a powerful and elegant way to estimate haplotype probabilities from observed genotypes. Uncertain (‘‘fuzzy’’) genotypes, like those resulting from AFLP scores, can be handled by adding an extra layer to the model. We describe the model and the estimation algorithm. We apply it to a data set of accurate human single nucleotide polymorphism (SNP) and to a data set of fuzzy tomato AFLP scores. Minimize

Contributors:

The Pennsylvania State University CiteSeerX Archives

Year of Publication:

2013-07-09

Source:

ftp://ftp.ncbi.nlm.nih.gov/pub/pmc/e6/b6/PLoS_One_2011_Sep_8_6(9)_e24219.tar.gz

ftp://ftp.ncbi.nlm.nih.gov/pub/pmc/e6/b6/PLoS_One_2011_Sep_8_6(9)_e24219.tar.gz 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:

Quantile smoothing of array CGH data

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Motivation: Plots of array Comparative Genomic Hybridization (CGH) data often show special patterns: stretches of constant level (copy number) with sharp jumps between them. There also can be much noise. Classic smoothing algorithms do not work well, because they introduce too much rounding. To remedy this, we introduce a fast and effective smoo...

Motivation: Plots of array Comparative Genomic Hybridization (CGH) data often show special patterns: stretches of constant level (copy number) with sharp jumps between them. There also can be much noise. Classic smoothing algorithms do not work well, because they introduce too much rounding. To remedy this, we introduce a fast and effective smoothing algorithm based on penalized quantile regression. It can compute arbitrary quantile curves, but we concentrate on the median to show the trend and the lower and upper quartile curves to show the spread of the data. Two-fold cross-validation is used for optimizing the weight of the penalties. Results: Simulated data and a published data set are used to show the capabilities of the method to detect segments of changed copy numbers in array CGH data. Availability: Software for R and Matlab is available. Contact: Minimize

Contributors:

The Pennsylvania State University CiteSeerX Archives

Year of Publication:

2008-07-17

Source:

http://bioinformatics.oxfordjournals.org/cgi/reprint/bti148v1.pdf

http://bioinformatics.oxfordjournals.org/cgi/reprint/bti148v1.pdf Minimize

Document Type:

text

Language:

en

Subjects:

Linear programming ; L1 norm ; cross-validation. 1

Linear programming ; L1 norm ; cross-validation. 1 Minimize

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

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Title:

Statistical Modelling 2008; 8(4): 385--401 Modelling general patterns of digit preference

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Abstract: In many applications data can be interpreted as indirect observations of a latent distri-bution. A typical example is the phenomenon known as digit preference, i.e. the tendency to round outcomes to pleasing digits. The composite link model (CLM) is a useful framework to uncover such latent distributions. Moreover, when applied to data...

Abstract: In many applications data can be interpreted as indirect observations of a latent distri-bution. A typical example is the phenomenon known as digit preference, i.e. the tendency to round outcomes to pleasing digits. The composite link model (CLM) is a useful framework to uncover such latent distributions. Moreover, when applied to data showing digit preferences, this approach allows estimation of the proportions of counts that were transferred to neighbouring digits. As the estimating equations generally are singular or severely ill-conditioned, we impose smoothness assumptions on the latent distribution and penalize the likelihood function. To estimate themisreported proportions, we use a weighted least-squares regression with an added L1 penalty. The optimal smoothing parameters are found by minimizing the Akaike’s information Criterion (AIC). The approach is verified by a simulation study and several applications are presented. Key words: composite link model; digit preference; L1 penalty; penalized likelihood; smoothing Minimize

Contributors:

The Pennsylvania State University CiteSeerX Archives

Year of Publication:

2015-03-05

Source:

http://www.demogr.mpg.de/publications/files/2945_1235560583_1_Camarda-Feb09-Sage.pdf

http://www.demogr.mpg.de/publications/files/2945_1235560583_1_Camarda-Feb09-Sage.pdf Minimize

Document Type:

text

Language:

en

DDC:

519 Probabilities & applied mathematics *(computed)*

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Title:

SUMMARY

Description:

Bayesian proportional hazards model with time-varying

Bayesian proportional hazards model with time-varying Minimize

Contributors:

The Pennsylvania State University CiteSeerX Archives

Year of Publication:

2008-07-01

Source:

http://www.stat.ucl.ac.be/ISpersonnel/lambert/LambertEilers2005BayesianProportionalHazardsModelWithTimeVaryingRegressionCoefficients.pdf

http://www.stat.ucl.ac.be/ISpersonnel/lambert/LambertEilers2005BayesianProportionalHazardsModelWithTimeVaryingRegressionCoefficients.pdf Minimize

Document Type:

text

Language:

en

Subjects:

KEY WORDS ; proportional hazards ; P-splines ; Markov chain Monte Carlo methods ; Metropolis-adjusted Langevin algorithm

KEY WORDS ; proportional hazards ; P-splines ; Markov chain Monte Carlo methods ; Metropolis-adjusted Langevin algorithm Minimize

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Title:

Multidimensional density smoothing with P -splines

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Abstract: We propose a simple and effective multidimensional density estima-tor. Our approach is essentially penalized Poisson regression using a rich tensor product B-spline basis, where the Poisson counts come from processing the data into a multidimensional histogram, often consisting of thousands of bins. The penalty enforces smoothness of t...

Abstract: We propose a simple and effective multidimensional density estima-tor. Our approach is essentially penalized Poisson regression using a rich tensor product B-spline basis, where the Poisson counts come from processing the data into a multidimensional histogram, often consisting of thousands of bins. The penalty enforces smoothness of the B-spline coefficients, specifically within the rows, columns, layers–depending on the dimension. In this paper, we focus on how a one-dimensional P-spline density estimator can be extended to two-dimensions, and beyond. In higher dimensions we provide a hint on how efficient grid algo-rithms can be implemented using array regression. Our approach optimizes the penalty weight parameter(s) using information criteria, specifically AIC. Two examples illustrate our method in two-dimensions. Minimize

Contributors:

The Pennsylvania State University CiteSeerX Archives

Year of Publication:

2015-01-11

Source:

http://www.macs.hw.ac.uk/~iain/research/GLAM/Galway_EM/Galway_EM.pdf

http://www.macs.hw.ac.uk/~iain/research/GLAM/Galway_EM/Galway_EM.pdf Minimize

Document Type:

text

Language:

en

Subjects:

AIC ; Effective Dimension ; Tensor Product

AIC ; Effective Dimension ; Tensor Product Minimize

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

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