منابع مشابه
Bayesian isotonic density regression.
Density regression models allow the conditional distribution of the response given predictors to change flexibly over the predictor space. Such models are much more flexible than nonparametric mean regression models with nonparametric residual distributions, and are well supported in many applications. A rich variety of Bayesian methods have been proposed for density regression, but it is not c...
متن کاملOptimal Reduced Isotonic Regression
Isotonic regression is a shape-constrained nonparametric regression in which the ordinate is a nondecreasing function of the abscissa. The regression outcome is an increasing step function. For an initial set of n points, the number of steps in the isotonic regression, m, may be as large as n. As a result, the full isotonic regression has been criticized as overfitting the data or making the re...
متن کاملOnline Isotonic Regression
We consider the online version of the isotonic regression problem. Given a set of linearly ordered points (e.g., on the real line), the learner must predict labels sequentially at adversarially chosen positions and is evaluated by her total squared loss compared against the best isotonic (nondecreasing) function in hindsight. We survey several standard online learning algorithms and show that n...
متن کاملSemiparametric additive isotonic regression
Article history: Received 15 November 2007 Received in revised form 4 September 2008 Accepted 4 September 2008 Available online 5 October 2008
متن کاملFastest Isotonic Regression Algorithms
The notes and tables below are for the information I had as of July 2015. However, I’ve had multiple requests to list fast expected case algorithms, not just ones fastest in the worst case; for implementations of the algorithms; and for evaluations of which algorithms are fastest in practice. These points are probably more useful than just listings of which algorithms are fastest in O-notation....
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ژورنال
عنوان ژورنال: Electronic Journal of Statistics
سال: 2017
ISSN: 1935-7524
DOI: 10.1214/17-ejs1365