Graphical Finite Population Sampling
Fuente:
arXiv
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| Format: | Preprint |
| Published: |
2023
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| _version_ | 1866908612932141056 |
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| author | Panahbehagh, Bardia |
| author_facet | Panahbehagh, Bardia |
| contents | This paper introduces an innovative and intuitive finite population sampling method that has been developed using a unique graphical framework. In this approach, first-order inclusion probabilities are represented as bars on a two-dimensional graph. By manipulating the positions of these bars, researchers can create a wide range of different sampling designs. This graphical visualization of sampling designs facilitates the exploration of alternative designs and may simplify certain aspects of the implementation compared to traditional mathematical algorithms. This novel approach holds significant promise for tackling complex challenges in sampling, such as achieving an optimal design. By applying a version of the greedy best-first search algorithm to this graphical approach, the potential for integrating intelligent algorithms into finite population sampling is demonstrated. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2308_07715 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Graphical Finite Population Sampling Panahbehagh, Bardia Statistics Theory Methodology This paper introduces an innovative and intuitive finite population sampling method that has been developed using a unique graphical framework. In this approach, first-order inclusion probabilities are represented as bars on a two-dimensional graph. By manipulating the positions of these bars, researchers can create a wide range of different sampling designs. This graphical visualization of sampling designs facilitates the exploration of alternative designs and may simplify certain aspects of the implementation compared to traditional mathematical algorithms. This novel approach holds significant promise for tackling complex challenges in sampling, such as achieving an optimal design. By applying a version of the greedy best-first search algorithm to this graphical approach, the potential for integrating intelligent algorithms into finite population sampling is demonstrated. |
| title | Graphical Finite Population Sampling |
| topic | Statistics Theory Methodology |
| url | https://arxiv.org/abs/2308.07715 |