Ridge Regression and James-Stein Estimation: Review and Comments

Draper, Norman R.; Nostrand, R. Craig Van

doi:10.1080/00401706.1979.10489815

Cited by 164 publications

(42 citation statements)

References 52 publications

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“…A fuller review is given in Draper and Van Nostrand (1979), Hocking (1976) and Judge et al (1985). A fuller review is given in Draper and Van Nostrand (1979), Hocking (1976) and Judge et al (1985).…”

Section: Bias and Variance Of Estimatesmentioning

confidence: 99%

Regression Analysis

Sen¹,

Srivastava²

1990

Springer Texts in Statistics

114

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Section: Bias and Variance Of Estimatesmentioning

confidence: 99%

Regression Analysis

Sen¹,

Srivastava²

1990

Springer Texts in Statistics

114

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“…The L 1 and L 2 penalty forms are similar to the penalties used for shrinkage in lasso and ridge regression respectively [22,23]. …”

Section: Resultsmentioning

confidence: 99%

Detecting DNA regulatory motifs by incorporating positional trends in information content

2004

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all media for any purpose, provided this notice is preserved along with the article's original URL. Detecting DNA regulatory motifs by incorporating positional trends in information content

On the basis of the observation that conserved positions in transcription factor binding sites are often clustered together, we propose a simple extension to the model-based motif discovery methods. We assign position-specific prior distributions to the frequency parameters of the model, penalizing deviations from a specified conservation profile. Examples with both simulated and real data show that this extension helps discover motifs as the data become noisier or when there is a competing false motif.

AbstractOn the basis of the observation that conserved positions in transcription factor binding sites are often clustered together, we propose a simple extension to the model-based motif discovery methods. We assign position-specific prior distributions to the frequency parameters of the model, penalizing deviations from a specified conservation profile. Examples with both simulated and real data show that this extension helps discover motifs as the data become noisier or when there is a competing false motif.

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“…A simple way to obtain_this is to add a multiple of the pa x p2 identity matrix I to E 2 2 before inverting it. This technique is called ridge regression , and is the subject matter of [8], [21], [22], and many other papers. Because (10) In our application x is surely not Gaussian.…”

Section: Predictionmentioning

confidence: 99%

“…These two areas converge in part in their concluding that the inversion and prediction problems can both be improved by, in effect, reducing the ratio between large eigenvalues and small ones, all necessarily real, of the matrix whose inverse figures in best linear unbiased estimation. Variations of how to do that are sometimes termed "ridge regression" [8]. We show that a very simple version of ridge regression with validated choice of ridge parameter not only improves prediction in P-PTSVQ but also diminishes coding error.…”

Section: Introductionmentioning

confidence: 99%

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Pruned tree-structured vector quantization of medical images with segmentation and improved prediction

Poggi¹,

Olshen

1995

IEEE Trans. on Image Process.

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The authors use predictive pruned tree-structured vector quantization for the compression of medical images. Their goal is to obtain a high compression ratio without impairing the image quality, at least so far as diagnostic purposes are concerned. The authors use a priori knowledge of the class of images to be encoded to help them segment the images and thereby to reserve bits for diagnostically relevant areas. Moreover, the authors improve the quality of prediction and encoding in two additional ways: by increasing the memory of the predictor itself and by using ridge regression for prediction. The improved encoding scheme was tested via computer simulations on a set of mediastinal CT scans; results are compared with those obtained using a more conventional scheme proposed recently in the literature. There were remarkable improvements in both the prediction accuracy and the encoding quality, above and beyond what comes from the segmentation. Test images were encoded at 0.5 bit per pixel and less without any visible degradation for the diagnostically relevant region.

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Ridge Regression and James-Stein Estimation: Review and Comments

Cited by 164 publications

References 52 publications

Regression Analysis

Regression Analysis

Detecting DNA regulatory motifs by incorporating positional trends in information content

Pruned tree-structured vector quantization of medical images with segmentation and improved prediction

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