OBSERVATION SUBAREA DECISION AND POPULATION DENSITY ESTIMATION BY SPACE SCALE-INVARIANCE

H. Kishino
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Abstract

In this paper, we consider the estimation problem of the population density of organisms in an area. We formulate this biometric problem as a variant of the sequential estimation problem of the intensity of the Poisson process and propose a method for simultaneously optimizing both the decision of an observation subarea and the estimation. By request from the application side, we define the equivariance of a procedure composed of a decision rule of an observation subarea and an estimator of population density under the scale transformations of an observation area, and then construct a scale-equivariant procedure. As a result of using the invariance principle, we utilize the framework of statistical decision theory, not the conventional framework of sequential estimation using asymptotic methods, and discuss the admissibility and minimaxity of our proposed procedure.
基于空间尺度不变性的观测分区决策与人口密度估计
本文研究了一个区域内生物种群密度的估计问题。我们将这一生物特征问题表述为泊松过程强度序列估计问题的一个变体,并提出了一种同时优化观测子区域的决策和估计的方法。根据应用方的要求,定义了观测区域尺度变换下由观测子区域决策规则和人口密度估计量组成的过程的等方差,构造了一个尺度等方差过程。由于使用了不变性原理,我们利用了统计决策理论的框架,而不是使用渐近方法的序列估计的传统框架,并讨论了我们提出的过程的可容许性和极小性。
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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