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Assessing Monotonicity: An Approach Based on Transformed Order Statistics. / Chen, Aleksandr ; Грибкова, Надежда Викторовна; Zitikis, Ričardas.
в: Mathematical Methods of Statistics, Том 33, № 1, 25.04.2024, стр. 79-94.Результаты исследований: Научные публикации в периодических изданиях › статья › Рецензирование
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TY - JOUR
T1 - Assessing Monotonicity: An Approach Based on Transformed Order Statistics
AU - Chen, Aleksandr
AU - Грибкова, Надежда Викторовна
AU - Zitikis, Ričardas
N1 - Chen, A., Gribkova, N. & Zitikis, R. Assessing Monotonicity: An Approach Based on Transformed Order Statistics. Math. Meth. Stat. 33, 79–94 (2024). https://doi.org/10.3103/S1066530724700054
PY - 2024/4/25
Y1 - 2024/4/25
N2 - In a number of research areas, such as non-convex optimization and machine learning, determining and assessing regions of monotonicity of functions is pivotal. Numerically, it can be done using the proportion of positive (or negative) increments of transformed ordered inputs. When the number of inputs grows, the proportion tends to an index of increase (or decrease) of the underlying function. In this paper, we introduce a most general index of monotonicity and provide its interpretation in all practically relevant scenarios, including those that arise when the distribution of inputs has jumps and flat regions, and when the function is only piecewise differentiable. This enables us to assess monotonicity of very general functions under particularly mild conditions on the inputs.
AB - In a number of research areas, such as non-convex optimization and machine learning, determining and assessing regions of monotonicity of functions is pivotal. Numerically, it can be done using the proportion of positive (or negative) increments of transformed ordered inputs. When the number of inputs grows, the proportion tends to an index of increase (or decrease) of the underlying function. In this paper, we introduce a most general index of monotonicity and provide its interpretation in all practically relevant scenarios, including those that arise when the distribution of inputs has jumps and flat regions, and when the function is only piecewise differentiable. This enables us to assess monotonicity of very general functions under particularly mild conditions on the inputs.
KW - monotonicity
KW - function
KW - index of increase
KW - order statistics
KW - function
KW - index of increase
KW - monotonicity
KW - order statistic
UR - https://www.mendeley.com/catalogue/2e1b29d1-1727-34e9-b7c2-d17ef631c885/
U2 - 10.3103/S1066530724700054
DO - 10.3103/S1066530724700054
M3 - Article
VL - 33
SP - 79
EP - 94
JO - Mathematical Methods of Statistics
JF - Mathematical Methods of Statistics
SN - 1066-5307
IS - 1
ER -
ID: 119315310