منابع مشابه
Nearly-Isotonic Regression
We consider the problem of approximating a sequence of data points with a “nearly-isotonic”, or nearly-monotone function. This is formulated as a convex optimization problem that yields a family of solutions, with one extreme member being the standard isotonic regression fit. We devise a simple algorithm to solve for the path of solutions, which can be viewed as a modified version of the well-k...
متن کاملBayesian 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
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ژورنال
عنوان ژورنال: Technometrics
سال: 2011
ISSN: 0040-1706,1537-2723
DOI: 10.1198/tech.2010.10111