arXiv subjects

Raphaël Jauslin

Publications and source records attributed to Raphaël Jauslin.

6 recordsLinked to original sources

arXiv2022

Sequential Spatially Balanced Sampling

Sequential sampling occurs when the entire population is not known in advance and data are obtained one at a time or in groups of units. This manuscript proposes a new algorithm to sequentially select a balanced sample. The algorithm respects equal and unequal inclusion probabilities. The method can also be used to select a spatially balanced sample if the population of interest contains spatial coordinates. A simulation study is proposed and the results show that the proposed method outperforms other methods.

stat.ME↗

arXiv2022

An Efficient Approach for Statistical Matching of Survey Data Trough Calibration, Optimal Transport and Balanced Sampling

Statistical matching aims to integrate two statistical sources. These sources can be two samples or a sample and the entire population. If two samples have been selected from the same population and information has been collected on different variables of interest, then it is interesting to match the two surveys to analyse, for example, contingency tables or covariances. In this paper, we propose an efficient method for matching two samples that may each contain a weighting scheme. The method matches the records of the two sources. Several variants are proposed in order to create a directly usable file integrating data from both information sources.

stat.ME↗

arXiv2021

Stream Sampling with Immediate Decision

The manuscript introduces a method to select a random sample from a stream by deciding on each sampling unit immediately after observing it. The process could be applied to unequal as well as equal probability sampling. The implementation is straightforward. Algorithm selects a unit in the sample based on a single condition. It is particularly effective to make direct decisions on stream data, despite the data arriving in groups or the stream being linear.

cs.DS↗

arXiv2021

Sequential Unequal Probability Sampling For Stream Population

A new unequal probability sampling method is proposed. This method is sequential. The decision to select or not each unit is made based on the order in which the units appear. A variant of this method allows selecting a sample from a stream. At each step, the decision to take the units successively according to the order of appearance in the stream is made. This method involves using a sliding window that is as small as possible. The method also allows the sample to be spread and even the level of spreading to be adjusted.

stat.ME↗

arXiv2021

Enhanced Cube Implementation For Highly Stratified Population

A balanced sampling design should always be the adopted strategies if auxiliary information is available. Besides, integrating a stratified structure of the population in the sampling process can considerably reduce the variance of the estimators. We propose here a new method to handle the selection of a balanced sample in a highly stratified population. The method improves substantially the commonly used sampling design and reduces the time-consuming problem that could arise if inclusion probabilities within strata do not sum to an integer.

stat.ME↗

arXiv2020

Spatial Spread Sampling Using Weakly Associated Vectors

Geographical data are generally autocorrelated. In this case, it is preferable to select spread units. In this paper, we propose a new method for selecting well-spread samples from a finite spatial population with equal or unequal inclusion probabilities. The proposed method is based on the definition of a spatial structure by using a stratification matrix. Our method exactly satisfies given inclusion probabilities and provides samples that are very well-spread. A set of simulations shows that our method outperforms other existing methods such as the Generalized Random Tessellation Stratified (GRTS) or the Local Pivotal Method (LPM). Analysis of the variance on a real dataset shows that our method is more accurate than these two. Furthermore, a variance estimator is proposed.

stat.ME↗