Book ; Online: Best $L_p$ Isotonic Regressions, $p \in \{0, 1, \infty\}$
2023
Abstract: Given a real-valued weighted function $f$ on a finite dag, the $L_p$ isotonic regression of $f$, $p ... in [0,\infty]$, is unique except when $p \in [0,1] \cup \{\infty\}$. We are interested in determining ... a ``best'' isotonic regression for $p \in \{0, 1, \infty\}$, where by best we mean a regression satisfying ...
| Abstract | Given a real-valued weighted function $f$ on a finite dag, the $L_p$ isotonic regression of $f$, $p \in [0,\infty]$, is unique except when $p \in [0,1] \cup \{\infty\}$. We are interested in determining a ``best'' isotonic regression for $p \in \{0, 1, \infty\}$, where by best we mean a regression satisfying stronger properties than merely having minimal norm. One approach is to use strict $L_p$ regression, which is the limit of the best $L_q$ approximation as $q$ approaches $p$, and another is lex regression, which is based on lexical ordering of regression errors. For $L_\infty$ the strict and lex regressions are unique and the same. For $L_1$, strict $q \scriptstyle\searrow 1$ is unique, but we show that $q \scriptstyle\nearrow 1$ may not be, and even when it is unique the two limits may not be the same. For $L_0$, in general neither of the strict and lex regressions are unique, nor do they always have the same set of optimal regressions, but by expanding the objectives of $L_p$ optimization to $p < 0$ we show $p{ \scriptstyle \nearrow} 0$ is the same as lex regression. We also give algorithms for computing the best $L_p$ isotonic regression in certain situations. |
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| Keywords | Computer Science - Discrete Mathematics ; Computer Science - Data Structures and Algorithms ; Mathematics - Statistics Theory |
| Subject code | 519 |
| Publishing date | 2023-05-31 |
| Publishing country | us |
| Document type | Book ; Online |
| Database | BASE - Bielefeld Academic Search Engine (life sciences selection) |
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