Kernal density functions to estimate parameters to simulate stochastic variables with sparse data: what is the best distribution?

机译：内核密度函数可估计参数以模拟具有稀疏数据的随机变量：最佳分布是什么？

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The purpose of this paper was to compare the goodness-of-fit for several parametric and kernal-based distributions to determine which distribution would perform well for simulating continuous random input variables whose underlying distributions were unknown. A Monte Carlo simulation procedure was developed to estimate how well some proxy distributions performed at approximating the distributions of random input variables. We conclude that without any a priori information on which to pick a probability distribution, the distribution for simulating a random input variable with limited specifications was a Parzen kernal distribution.

机译：本文的目的是比较几种基于参数和基于内核的分布的拟合优度，以确定哪种分布在模拟基础分布未知的连续随机输入变量时表现良好。开发了蒙特卡罗模拟程序来估计一些代理分布在逼近随机输入变量的分布方面表现如何。我们得出的结论是，在没有任何先验信息可以选择概率分布的情况下，用于模拟具有有限规格的随机输入变量的分布为Parzen核分布。

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来源
《Risk analysis VII: simulation and hazard mitigation amp; Brownfields V: prevention, assessment, rehabilitation and development of brownfield sites》|2010年|PI.3-PI.12|共10页
会议地点 Algarve(PT);Algarve(PT);Algarve(PT)
作者
J. W. Richardson; J. L. Outlaw; K. Schumann;
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作者单位

Department of Agricultural Economics, Texas AM University, USA;

Department of Agricultural Economics, Texas AM University, USA;

Welch Consulting, USA;

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会议组织
原文格式 PDF
正文语种 eng
中图分类
关键词
probability distribution selection; kernal distributions; simulation; simetar;

机译：概率分布选择；内核分布；模拟; Simetar;

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Kernal density functions to estimate parameters to simulate stochastic variables with sparse data: what is the best distribution?

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