Efficient unequal probability resampling from finite populations

Conti, Pier Luigi; Mecatti, Fulvia; Nicolussi, Federica

doi:10.1016/j.csda.2021.107366

Cited by 4 publications

(3 citation statements)

References 14 publications

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“…When choosing a specific model to fill in the gaps, one should take into account the possibilities of its application, which significantly depend on the data analysis method that is supposed to be used in the future. There are various approaches to handle missing information, such as EM-estimation, Hot Deck, Zet, Barlet's method, Resampling, ZET braid [7,8].…”

Section: Methodsmentioning

confidence: 99%

Models and algorithms for human capital reproduction intellectual analysis

2023

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The managerial decisions making tasks in human capital reproduction complex systems are solved on the basis of models built on experimental data. It is problematic to take into account all the factors affecting the human capital reproduction. Existing approaches are not focused on building models for the human capital reproduction with incomplete information. Algorithms for inductive modeling are developed for the human capital reproduction systems characteristics functional description. The software is developed to implement the proposed algorithms for the human capital reproduction intellectual analysis based on the metric spaces of multisets.

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Section: Methodsmentioning

confidence: 99%

Models and algorithms for human capital reproduction intellectual analysis

2023

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“…Computational shortcuts, based on ideas similar to those in [28], but based on correct approximations of first order inclusion probabilities, were developed in [29] for descriptive, design-based inference. In particular, in that paper, methodologies based on drawings with replacements from the original sample were proposed, and their merits, from both a theoretical and a computational point of view, were studied.…”

Section: Computational Issuesmentioning

confidence: 99%

Resampling under Complex Sampling Designs: Roots, Development and the Way Forward

Conti

Mecatti

2022

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In the present paper, resampling for finite populations under an iid sampling design is reviewed. Our attention is mainly focused on pseudo-population-based resampling due to its properties. A principled appraisal of the main theoretical foundations and results is given and discussed, together with important computational aspects. Finally, a discussion on open problems and research perspectives is provided.

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“…Jiaoyun et al proposed a method for estimating the sampling boundary under the condition that the probability distribution is constrained (Jiaoyun, Zhenyu, Junda, & Lian, 2022). Conti et al proposed a resampling method that is easy to compute (Conti, Mecatti, & Nicolussi, 2022). Modak proposed a method to determine the number of cluster paths (Modak, 2022).…”

Section: Introductionmentioning

confidence: 99%

On Discrete Probability Distributions to Grasp the Number of Samples in a Population

Yabu¹

2023

Preprint

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In situations where there are multiple options, when the number of options selected is known, the discrete probability distribution of the number of options selected is mathematically defined, and properties such as expected value and variance are shown. By using the discrete probability distributions derived in this paper, we can find the most probable number of people trying to buy and the most probable number of sales of other people's products when we know the number of sales of our own product. Therefore, application in the field of economics can be expected.

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Efficient unequal probability resampling from finite populations

Cited by 4 publications

References 14 publications

Models and algorithms for human capital reproduction intellectual analysis

Models and algorithms for human capital reproduction intellectual analysis

Resampling under Complex Sampling Designs: Roots, Development and the Way Forward

On Discrete Probability Distributions to Grasp the Number of Samples in a Population

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