1,720,993 research outputs found
Bootstrap algorithms for risk models with auxiliary variable and complex samples
No full textLo scopo del presente lavoro è 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 è raro il caso in cui alcuni animali, già catturati una volta, mostrino una accresciuta familiarità nei confronti del contatto umano, mentre altri tendano a nascondersi. In questi casi, le probabilità di inclusione possono risultare modificate. In questo lavoro si presentano due applicazioni dell’algoritmo bootstrap per il campionamento nto πPS proposto da Mecatti (2000) adattate al caso del campionamento per cattura-ricattura. La prima riguarda la stima della varianza dell’usuale stimatore di Petersen della numerosità della popolazione. La seconda utilizza la stessa stima come numerosità delle popolazioni empiriche bootstrap su cui si basa l’algoritmo di Mecatti. Il lavoro si conclude con due simulazioni su dati real
Rouding non-integer weights in bootstrapping non IID samples : actual problem or harmless practice?
No full textThe effect of the practice of rounding non-integer weights in bootstrapping complex sam-ples form finite populations is investigated by means of an extended simulation study. The extent to which rounding can interfere with basic bootstrap principles as well as with the formal properties of the final bootstrap estimate are evaluated. Indications and recommendations on application of this method are discussed
Analytic inference in finite population framework via resampling
Full text linkThe aim of this dissertation is to provide nonparametric tools for analytic inference
on superpopulation models. To pursue the goal we approach to the problem in two
different ways. The first one is analytic. Following the classical empirical process
theory, we first derive a functional central limit theorem that fully characterizes
the asymptotic distribution of the Hàjek estimator of the distribution function of
the superpopulation. In addition, assuming some regularity conditions on the (superpopulation)
parameters of interest, we extend this analytic characterization to
a large class of possible paramaters of interest. The second one is more “practical”:
our aim is to construct a computer intensive procedure that allows to infer the
superpopulation, also when the (asymptotic) distribution of an interest parameter
has an unmanageable analytic form. Clearly, such a procedure is resampling. Unfortunately,
the most famous resampling technique, the bootstrap procedure, does
not work in our framework. In fact, in the finite population framework, even if a
superpopulation is assumed, the units cannot be assumed independent in the presence
of a non trivial sampling design. This fact makes the classic bootstrap fail.
Of course, in the survey sampling literature, resampling procedure have been proposed,
but we haven not resort to them because of two reasons: i) a largest part of
these resampling techniques have been developed to infer the finite population and
not the superpopulation; ii) we want to make a parallel between the classical non
parametric theory and survey sampling. Almost all of these procedures are justified
by mimicking the first two moments of the distribution of the considered estimator,
and this is not the argument used to justify Efron’s bootstrap in classical nonparametric
statistics. Thus, we introduce the “ multinomial” scheme as a resampling
procedure for the superpopulation and we provide an asymptotic validation of this
method, that involves the whole distribution of the considered estimators, exactly
as it happens for classic bootstrap. In the last part of this work, the results obtained
are applied to different inferential problems and, for each one of the concerned problem,
a simulation study is performed to test the validity of our proposal. For these
applications, we especially focused on problems where the interest parameter is not
a linear function of the data
Bootstrap algorithms for variance estimation in πPS sampling
No full textThe problem of bootstrapping the estimator's variance under a probability proportional to size design is examined. Focusing on the Horvitz-Thompson estimator, three πPS-bootstrap algorithms are introduced 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 investigate the properties of the provided bootstrap variance estimator
Bootstrap algorithms for risk models with auxiliary variable and complex samples
Full text linkResampling 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
Full text linkLo scopo del presente lavoro è 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 è raro il caso in cui alcuni animali, già catturati una volta, mostrino una accresciuta familiarità nei confronti del contatto umano, mentre altri tendano a nascondersi. In questi casi, le probabilità di inclusione possono risultare modificate. In questo lavoro si presentano due applicazioni dell’algoritmo bootstrap per il campionamento PS π proposto da Mecatti (2000) adattate al caso del campionamento per cattura-ricattura. La prima riguarda la stima della varianza dell’usuale stimatore di Petersen della numerosità della popolazione. La seconda utilizza la stessa stima come numerosità delle popolazioni empiriche bootstrap su cui si basa l’algoritmo di Mecatti. Il lavoro si conclude con due simulazioni su dati real
- …
