Results 291 to 300 of about 7,132,308 (328)

Topological modeling and QSPR based prediction of physicochemical properties of bioactive polyphenols. [PDF]

Sci Rep
Hakeem A, Ullah A, Zaman S, Mahmoud EE, Ahmad H, Ali P, Belay MB.   +6 more
europepmc   +1 more source

Prediction model for the selection of patients with glioma to proton therapy. [PDF]

Acta Oncol
Folsted Kallehauge J, Grondahl S, Skinnerup Byskov C, Høyer M, Lukacova S.   +4 more
europepmc   +1 more source

Number of Publications on New Clinical Prediction Models: A Bibliometric Review.

JMIR Med Inform
Arshi B, Wynants L, Rijnhart E, Reeve K, Cowley LE, Smits LJ.   +5 more
europepmc   +1 more source

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Robust Regression

IEEE Transactions on Pattern Analysis and Machine Intelligence, 2012
Discriminative methods (e.g., kernel regression, SVM) have been extensively used to solve problems such as object recognition, image alignment and pose estimation from images. These methods typically map image features ( X) to continuous (e.g., pose) or discrete (e.g., object category) values. A major drawback of existing discriminative methods is that
Fernando De la Torre, Dong Huang, Ricardo Cabral   +2 more
openaire   +4 more sources

Boolean regression

Annals of Operations Research, 1995
We take a regression-based approach to the problem of induction, which is the problem of inferring general rules from specific instances. Whereas traditional regression analysis fits a numerical formula to data, we fit a logical formula to boolean data. We can, for instance, construct an expert system for fitting rules to an expert's observed behavior.
Boros, E., Hammer, P. L., Hooker, J. N.
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On regressing regression coefficients

Journal of Statistical Planning and Inference, 1982
This paper considers a regression model in which coefficients obtained from a previous regression are themselves the object of analysis. It is shown that the parameters of interest can be obtained in two ways: pooling across observations and subsamples, or a two-stage process of first estimating the coefficients within each subsample, and then using ...
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Regression and unbiased regression

Trabajos de Estadistica Y de Investigacion Operativa, 1978
A method of evaluation of the efficiency of a regression function is given. Unbiased regression is defined. In annex, a method of computation of mean values is given, which is very useful in many cases.
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Regressing and Regressed Melanoma

2004
Malignant melanoma frequently undergoes regression (10–35% of cases), and on occasion, this can be extensive. Regressed areas can vary from foci only a few rete ridges in breadth to being subtotal or even complete. Only about 40 cases of completely regressed primary melanoma are on record, but this is doubtless a huge underestimation.
Philip E. LeBoit, Guido Massi
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Regression Calibration in Failure Time Regression

Biometrics, 1997
In this paper we study a regression calibration method for failure time regression analysis when data on some covariates are missing or mismeasured. The method estimates the missing data based on the data structure estimated from a validation data set, a random subsample of the study cohort in which covariates are always observed.
C. Y. Wang, Ziding Feng, Li Hsu, Ross L. Prentice   +3 more
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Multiple Regression—Regression Diagnostics

2004
In Chapter 9 we show how to set up and produce an initial analysis of a regression model with several predictors. In the present chapter we discuss ways to investigate whether the model assumptions are met and, when the assumptions are not met, ways to revise the model to better conform with the assumptions. We also examine ways to assess the effect on
Burt Holland, Richard M. Heiberger
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