Efficient unequal probability resampling from finite populations

A resampling technique for probability-proportional-to size sampling designs is proposed. It is essentially based on a special form of variable probability, without replacement sampling applied directly to the sample data, yet according to the pseudo-population approach. From a theoretical point of view, it is asymptotically correct: as both the sample size and the population size increase, under mild regularity conditions the proposed resampling design tends to coincide with the original sampling design under which sample data were collected. From a computational point of view, the proposed methodology is easy to be implemented and efficient, because it neither requires the actual construction of the pseudo-population nor any form of randomization to ensure integer weights and sizes. Empirical evidence based on a simulation study1 indicates that the proposed resampling technique outperforms its two main competitors for confidence interval construction of various population parameters including quantiles. (c) 2021 Published by Elsevier B.V

Bootstrap algorithms for risk models with auxiliary variable and complex samples

Resampling methods are often invoked in risk modelling when the stability of estimators of model parameters has to be assessed. The accuracy of variance estimates is crucial since the operational risk management affects strategies, decisions and policies. However, auxiliary variables and the complexity of the sampling design are seldom taken into proper account in variance estimation. In this paper bootstrap algorithms for finite population sampling are proposed in presence of an auxiliary variable and of complex samples. Results from a simulation study exploring the empirical performance of some bootstrap algorithms are presente

Bootstrap methods for capture-recapture sampling

Lo scopo del presente lavoro \ue8 quello di estendere al caso del campionamento per cattura-ricattura il metodo bootstrap per la stima della varianza di stimatori costruiti su campioni da popolazioni finite. Nel campionamento da popolazioni di animali, non \ue8 raro il caso in cui alcuni animali, gi\ue0 catturati una volta, mostrino una accresciuta familiarit\ue0 nei confronti del contatto umano, mentre altri tendano a nascondersi. In questi casi, le probabilit\ue0 di inclusione possono risultare modificate. In questo lavoro si presentano due applicazioni dell\u2019algoritmo bootstrap per il campionamento PS \u3c0 proposto da Mecatti (2000) adattate al caso del campionamento per cattura-ricattura. La prima riguarda la stima della varianza dell\u2019usuale stimatore di Petersen della numerosit\ue0 della popolazione. La seconda utilizza la stessa stima come numerosit\ue0 delle popolazioni empiriche bootstrap su cui si basa l\u2019algoritmo di Mecatti. Il lavoro si conclude con due simulazioni su dati real

Bootstrap algorithms for variance estimation in complex survey sampling

The problem of estimating the variance of the Horvitz-Thompson estimator under a probability proportional to size design is concerned. Some IPPS-bootstrap algorithms are proposed with the purpose of both simplifying available procedures and of improving efficiency. Results from a simulation study using both natural and artificial data are presented in order to empirically study the bias and stability of the bootstrap variance estimators propose

Bootstrap methods for capture-recapture sampling

Lo scopo del presente lavoro \ue8 quello di estendere al caso del campionamento per cattura-ricattura il metodo bootstrap per la stima della varianza di stimatori costruiti su campioni da popolazioni finite. Nel campionamento da popolazioni di animali, non \ue8 raro il caso in cui alcuni animali, gi\ue0 catturati una volta, mostrino una accresciuta familiarit\ue0 nei confronti del contatto umano, mentre altri tendano a nascondersi. In questi casi, le probabilit\ue0 di inclusione possono risultare modificate. In questo lavoro si presentano due applicazioni dell\u2019algoritmo bootstrap per il campionamento PS \u3c0 proposto da Mecatti (2000) adattate al caso del campionamento per cattura-ricattura. La prima riguarda la stima della varianza dell\u2019usuale stimatore di Petersen della numerosit\ue0 della popolazione. La seconda utilizza la stessa stima come numerosit\ue0 delle popolazioni empiriche bootstrap su cui si basa l\u2019algoritmo di Mecatti. Il lavoro si conclude con due simulazioni su dati real

A Simplified Efficient and Direct Unequal Probability Resampling = Un semplice Ricampionamento, efficiente e diretto per campioni a probabilita variabili

In questo lavoro si introduce una tecnica di ricampionamento valida per disegni campionari con differenti probabilita di inclusione. L\u2019idea di base ` e di usare ` un disegno di ricampionamento di tipo ppswor. Le principalei proprieta del metodo ` sono studiate, e le relazioni con altre metodologie di ricampionamento sono discusse.In this paper, a new resampling technique for sampling designs with unequal inclusion probabilities is proposed. The basic idea is to use a resampling design based on ppswor. Its main properties are studied, and its relationships with other resampling methodologies are discussed

Welfare and quality of farmed trout fed high plant protein diets. 2 innovative killing method effect on stress and quality indicators

AbstractThe fish stunning/slaughtering procedure has an important role both for the ethical aspect related to animal welfare and for the potential quality of the final products. Stressful harvest procedure and killing methods can negatively influence the post mortem biochemical processes with a consequent faster fish freshness loss. In particular, killing procedures causing a long agony are not humane and can shorten fish shelf life; others, more humane, can have risks for the consumers health (anaesthetics or chemical substances) or are not feasible in small/medium size fish industry (spiking and knocking). The aim of this study was to compare the traditional killing method (asphyxia - A) used for rainbow trout (Onchorynchus mykiss) with an innovative stunning/slaughtering method (two-stage electric stun: 2s at a 500Hz electric field of 2.5V/cm and then 58s at a 50Hz electric field of 0.75V/cm - E), through the study of their effects on stress and quality indicators in fish fed with traditional fish meal..

Population Empirical Likelihood Estimation in Dual Frame Surveys

Dual frame surveys are a device to reduce the costs derived from data collection in surveys and improve coverage for the whole target population. Since their introduction, in the 1960’s, dual frame surveys have gained much attention and several estimators have been formulated based on a number of different approaches. In this work, we propose new dual frame estimators based on the population empirical likelihood method originally proposed by Chen and Kim (2014) and using both the dual and the single frame approach. The extension of the proposed methodology to more than two frame surveys is also sketched. The performance of the proposed estimators in terms of relative bias and relative mean squared error is tested through simulation experiments. These experiments indicate that the proposed estimators yield better results than other likelihood-based estimators proposed in the literature.Ministerio de Economía y Competitividad of Spai

Kernel-based methods for combining information of several frame surveys

A sample selected from a single sampling frame may not represent adequatly the entire population. Multiple frame surveys are becoming increasingly used and popular among statistical agencies and private organizations, in particular in situations where several sampling frames may provide better coverage or can reduce sampling costs for estimating population quantities of interest. Auxiliary information available at the population level is often categorical in nature, so that incorporating categorical and continuous information can improve the efficiency of the method of estimation. Nonparametric regression methods represent a widely used and flexible estimation approach in the survey context. We propose a kernel regression estimator for dual frame surveys that can handle both continuous and categorical data. This methodology is extended to multiple frame surveys. We derive theoretical properties of the proposed methods and numerical experiments indicate that the proposed estimator perform well in practical settings under different scenarios.Ministerio de Economía y CompetitividadConsejería de Economía, Innovación, Ciencia y Emple