Evaluating the complexity of some families of functional data

IF 0.7 4区数学 Q4 OPERATIONS RESEARCH & MANAGEMENT SCIENCE

Sort-Statistics and Operations Research Transactions Pub Date : 2018-06-19 DOI:10.2436/20.8080.02.67

E. Bongiorno, A. Goia, P. Vieu

引用次数: 3

Abstract

In this paper we study the complexity of a functional data set drawn from particular processes by means of a two-step approach. The first step considers a new graphical tool for assessing to which family the data belong: the main aim is to detect whether a sample comes from a monomial or an exponential family. This first tool is based on a nonparametric kNN estimation of small ball probability. Once the family is specified, the second step consists in evaluating the extent of complexity by estimating some specific indexes related to the assigned family. It turns out that the developed methodology is fully free from assumptions on model, distribution as well as dominating measure. Computational issues are carried out by means of simulations and finally the method is applied to analyse some financial real curves dataset.

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评估一些函数数据族的复杂性

本文采用两步法研究了从特定过程中提取的函数数据集的复杂性。第一步考虑一种新的图形工具来评估数据属于哪个族:主要目的是检测样本是来自单项式族还是指数族。第一个工具是基于小球概率的非参数kNN估计。一旦指定了家族，第二步包括通过估计与指定家族相关的一些特定指标来评估复杂性程度。结果表明，该方法完全摆脱了对模型、分布和主导测度的假设。通过仿真进行计算，最后将该方法应用于某金融实物曲线数据集的分析。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Sort-Statistics and Operations Research Transactions 管理科学-统计学与概率论

CiteScore

3.10

自引率

0.00%

发文量

审稿时长

>12 weeks

期刊介绍： SORT (Statistics and Operations Research Transactions) —formerly Qüestiió— is an international journal launched in 2003. It is published twice-yearly, in English, by the Statistical Institute of Catalonia (Idescat). The journal is co-edited by the Universitat Politècnica de Catalunya, Universitat de Barcelona, Universitat Autonòma de Barcelona, Universitat de Girona, Universitat Pompeu Fabra i Universitat de Lleida, with the co-operation of the Spanish Section of the International Biometric Society and the Catalan Statistical Society. SORT promotes the publication of original articles of a methodological or applied nature or motivated by an applied problem in statistics, operations research, official statistics or biometrics as well as book reviews. We encourage authors to include an example of a real data set in their manuscripts.