2009, Vol. 33, Núm. 1
http://hdl.handle.net/2099/8908
2016-12-05T10:35:02ZOn the performance of small-area estimators: fixed vs. random area parameters
http://hdl.handle.net/2099/8946
On the performance of small-area estimators: fixed vs. random area parameters
Costa, Àlex; Satorra, A.; Ventura, Eva
Most methods for small-area estimation are based on composite estimators derived from designor model-based methods. A composite estimator is a linear combination of a direct and an indirect estimator with weights that usually depend on unknown parameters which need to be estimated.
Although model-based small-area estimators are usually based on random-effects models, the assumption of fixed effects is at face value more appropriate. Model-based estimators are justified by the assumption of random area effects; in practice, however, areas can not be substituted for
one another in a random manner (we say, they are not interchangeable). In the present paper we empirically assess the quality of several small-area estimators in the setting in which the area effects are treated as fixed. We consider two settings: one that draws samples from a theoretical
population, and another that draws samples from an empirical population of a labour force register maintained by the National Institute of Social Security (NISS) of Catalonia. We distinguish two types of composite estimators: a) those that use weights that involve area specific estimates of bias and variance; and, b) those that use weights that involve a common variance and a common squared bias estimate for all the areas. We assess their precision and discuss alternatives to optimizing composite estimation in applications.
2010-04-27T15:24:01ZCosta, ÀlexSatorra, A.Ventura, EvaMost methods for small-area estimation are based on composite estimators derived from designor model-based methods. A composite estimator is a linear combination of a direct and an indirect estimator with weights that usually depend on unknown parameters which need to be estimated.
Although model-based small-area estimators are usually based on random-effects models, the assumption of fixed effects is at face value more appropriate. Model-based estimators are justified by the assumption of random area effects; in practice, however, areas can not be substituted for
one another in a random manner (we say, they are not interchangeable). In the present paper we empirically assess the quality of several small-area estimators in the setting in which the area effects are treated as fixed. We consider two settings: one that draws samples from a theoretical
population, and another that draws samples from an empirical population of a labour force register maintained by the National Institute of Social Security (NISS) of Catalonia. We distinguish two types of composite estimators: a) those that use weights that involve area specific estimates of bias and variance; and, b) those that use weights that involve a common variance and a common squared bias estimate for all the areas. We assess their precision and discuss alternatives to optimizing composite estimation in applications.A general procedure of estimating the population mean in the presence of non-response under double sampling using auxiliary information
http://hdl.handle.net/2099/8945
A general procedure of estimating the population mean in the presence of non-response under double sampling using auxiliary information
Singh, H. P.; Kumar, Sunil
In the present study, we propose a general class of estimators for population mean of the study variable in the presence of non-response using auxiliary information under double sampling. The expression of mean squared error (MSE) of the proposed class of estimators is derived under double (two-stage) sampling. Some estimators are also derived from the proposed class by
allocating the suitable values of constants used. Comparisons of the proposed strategy with the usual unbiased estimator and other estimators are carried out. The results obtained are illustrated numerically using an empirical sample considered in the literature.
2010-04-27T15:10:27ZSingh, H. P.Kumar, SunilIn the present study, we propose a general class of estimators for population mean of the study variable in the presence of non-response using auxiliary information under double sampling. The expression of mean squared error (MSE) of the proposed class of estimators is derived under double (two-stage) sampling. Some estimators are also derived from the proposed class by
allocating the suitable values of constants used. Comparisons of the proposed strategy with the usual unbiased estimator and other estimators are carried out. The results obtained are illustrated numerically using an empirical sample considered in the literature.Estimating unemployment in very small areas
http://hdl.handle.net/2099/8938
Estimating unemployment in very small areas
Ugarte, Maria Dolores; Goicoa, T.; Militino, Ana F.; Sagaseta-López, M.
In the last few years, European countries have shown a deep interest in applying small area techniques to produce reliable estimates at county level. However, the specificity of every European country and the heterogeneity of the available auxiliary information, make the use of a common methodology a very difficult task. In this study, the performance of several design-based, model-assisted, and model-based estimators using different auxiliary information for estimating unemployment at small area level is analyzed. The results are illustrated with data from Navarre,
an autonomous region located at the north of Spain and divided into seven small areas. After discussing pros and cons of the different alternatives, a composite estimator is chosen, because of its good trade-off between bias and variance. Several methods for estimating the prediction
error of the proposed estimator are also provided.
2010-04-26T18:54:19ZUgarte, Maria DoloresGoicoa, T.Militino, Ana F.Sagaseta-López, M.In the last few years, European countries have shown a deep interest in applying small area techniques to produce reliable estimates at county level. However, the specificity of every European country and the heterogeneity of the available auxiliary information, make the use of a common methodology a very difficult task. In this study, the performance of several design-based, model-assisted, and model-based estimators using different auxiliary information for estimating unemployment at small area level is analyzed. The results are illustrated with data from Navarre,
an autonomous region located at the north of Spain and divided into seven small areas. After discussing pros and cons of the different alternatives, a composite estimator is chosen, because of its good trade-off between bias and variance. Several methods for estimating the prediction
error of the proposed estimator are also provided.Modelling consumer credit risk via survival analysis
http://hdl.handle.net/2099/8937
Modelling consumer credit risk via survival analysis
Cao, Ricardo; Vilar Fernández, Juan Manuel; Devía, A.
Credit risk models are used by financial companies to evaluate in advance the insolvency risk caused by credits that enter into default. Many models for credit risk have been developed over the past few decades. In this paper, we focus on those models that can be formulated in terms of
the probability of default by using survival analysis techniques. With this objective three different mechanisms are proposed based on the key idea of writing the default probability in terms of the conditional distribution function of the time to default. The first method is based on a Cox’s
regression model, the second approach uses generalized linear models under censoring and
the third one is based on nonparametric kernel estimation, using the product-limit conditional distribution function estimator by Beran. The resulting nonparametric estimator of the default probability is proved to be consistent and asymptotically normal. An empirical study, based on
modified real data, illustrates the three methods.
2010-04-26T18:37:55ZCao, RicardoVilar Fernández, Juan ManuelDevía, A.Credit risk models are used by financial companies to evaluate in advance the insolvency risk caused by credits that enter into default. Many models for credit risk have been developed over the past few decades. In this paper, we focus on those models that can be formulated in terms of
the probability of default by using survival analysis techniques. With this objective three different mechanisms are proposed based on the key idea of writing the default probability in terms of the conditional distribution function of the time to default. The first method is based on a Cox’s
regression model, the second approach uses generalized linear models under censoring and
the third one is based on nonparametric kernel estimation, using the product-limit conditional distribution function estimator by Beran. The resulting nonparametric estimator of the default probability is proved to be consistent and asymptotically normal. An empirical study, based on
modified real data, illustrates the three methods.