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Journal ArticleDOI

The Isotonic Regression Problem and its Dual

TLDR
In this article, a generalization of the Fenchel dual isotonic regression problem has been proposed to solve the problem of inventory theory and statistics, and a function of the isotonic regressions has been defined to solve these problems.
Abstract
The isotonic regression problem is to minimize Σt i = 1 [gi − xi]2wi subject to xi ≤ xj when where wi>0 and gi (i= 1, 2, …, k) are given and is a specified partial ordering on {1, 2, …, k}. The solution is called the isotonic regression on g. We formulate a generalization of this problem and calculate its Fenchel dual. A function of the isotonic regression also solves these problems. Problems in inventory theory and statistics are identified as dual isotonic regression problems.

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Citations
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Journal ArticleDOI

An Algorithm for Restricted Least Squares Regression

TL;DR: In this article, a simple iterative algorithm is presented and shown to converge to the desired solution for minimizing a least square expression subject to side constraints, which is a commonly occurring problem in statistics.
Journal ArticleDOI

Boosting the accuracy of differentially private histograms through consistency

TL;DR: It is shown that it is possible to significantly improve the accuracy of a general class of histogram queries while satisfying differential privacy, and that these techniques can be used for estimating the degree sequence of a graph very precisely, and for computing a histogram that can support arbitrary range queries accurately.
Book ChapterDOI

A Method for Finding Projections onto the Intersection of Convex Sets in Hilbert Spaces

TL;DR: In this article, it is shown that if the constraint region can be expressed as a finite intersection of simpler convex regions, then one can obtain the projection onto the intersection by performing a series of projections only onto the simpler regions.
Proceedings ArticleDOI

Sharing graphs using differentially private graph models

TL;DR: This work develops a differentially-private graph model that produces synthetic graphs that closely match the originals in both graph structure metrics and behavior in application-level tests, and applies this model to Internet, web, and Facebook social graphs.
Journal ArticleDOI

Additive structure in qualitative data: An alternating least squares method with optimal scaling features

TL;DR: In this article, the additive structure of data that can be measured at the nominal, ordinal or cardinal levels, may be obtained from either a discrete or continuous source, and may have known degrees of imprecision.
References
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Journal ArticleDOI

Multidimensional scaling by optimizing goodness of fit to a nonmetric hypothesis

TL;DR: The fundamental hypothesis is that dissimilarities and distances are monotonically related, and a quantitative, intuitively satisfying measure of goodness of fit is defined to this hypothesis.
Journal ArticleDOI

Nonmetric multidimensional scaling: A numerical method

TL;DR: The numerical methods required in the approach to multi-dimensional scaling are described and the rationale of this approach has appeared previously.
Journal ArticleDOI

An empirical distribution function for sampling with incomplete information

TL;DR: In this article, it was shown that the consistency property of the maximum likelihood estimators depends on a grouping of observations which might very well appeal to an investigator on purely intuitive grounds.
Journal ArticleDOI

Maximum Likelihood Estimates of Monotone Parameters

TL;DR: In this article, the maximum likelihood estimators of distribution parameters subject to certain order relations are determined for simultaneous sampling from a number of populations, when the order relations may be specified by regarding the distribution parameters, of which one is associated with each population.
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