DSpace Collection:
http://hdl.handle.net/2099/8907
Wed, 16 Apr 2014 22:49:23 GMT2014-04-16T22:49:23Zwebmaster.bupc@upc.eduUniversitat Politècnica de Catalunya. Servei de Biblioteques i DocumentaciónoSampling design variance estimation of small area estimators in the Spanish Labour Force Survey
http://hdl.handle.net/2099/8936
Title: Sampling design variance estimation of small area estimators in the Spanish Labour Force Survey
Authors: Herrador, M.; Morales, Domingo; Esteban, M. D.; Sánchez, Ángel; Santamaría Arana, Laureano; Marhuenda, Y.; Pérez, A.
Abstract: The main goal of this paper is to Investigate how to estimate sampling design variances of modelbased and model-assisted small area estimators in a complex survey sampling setup. For this purpose the Spanish Labour Force Survey is considered. Sample and aggregated data are taken from the
Canary Islands in the second trimester of 2003 in order to obtain some small area estimators of ILO unemployment totals. Several problems arising from the application of standard small area estimation
procedures to the survey are described. It is shown that standard variance estimators based on explicit formulas are not applicable in the strict sense, since the assumptions under which they are derived do not hold. In addition two resampling techniques, bootstrap and jackknife, are considered. These methods treat all the considered estimators in the same manner and therefore they can be used as performance measures to compare them. From the analysis of the obtained results, some ecommendations are given.Tue, 01 Jan 2008 00:00:00 GMThttp://hdl.handle.net/2099/89362008-01-01T00:00:00ZHerrador, M.; Morales, Domingo; Esteban, M. D.; Sánchez, Ángel; Santamaría Arana, Laureano; Marhuenda, Y.; Pérez, A.noLabour Force Survey, Small area estimation, Linear models, Mean squared error, Bootstrap, Jackknife, Unemployment totals, Calibrated weightsThe main goal of this paper is to Investigate how to estimate sampling design variances of modelbased and model-assisted small area estimators in a complex survey sampling setup. For this purpose the Spanish Labour Force Survey is considered. Sample and aggregated data are taken from the
Canary Islands in the second trimester of 2003 in order to obtain some small area estimators of ILO unemployment totals. Several problems arising from the application of standard small area estimation
procedures to the survey are described. It is shown that standard variance estimators based on explicit formulas are not applicable in the strict sense, since the assumptions under which they are derived do not hold. In addition two resampling techniques, bootstrap and jackknife, are considered. These methods treat all the considered estimators in the same manner and therefore they can be used as performance measures to compare them. From the analysis of the obtained results, some ecommendations are given.On equivalence and bioequivalence testing
http://hdl.handle.net/2099/8935
Title: On equivalence and bioequivalence testing
Authors: Ocaña i Rebull, Jordi; Sánchez Olavarría, María Pilar; Sánchez, Alex; Carrasco Jordan, Josep Lluís
Abstract: Equivalence testing is the natural approach to many statistical problems. First, its main application, bioequivalence testing, is reviewed. The basic concepts of bioequivalence testing (2×2 crossover
designs, TOST, interval inclusion principle, etc.) and its problems (TOST biased character, the carryover problem, etc.) are considered. Next, equivalence testing is discussed more generally. Some applications and methods are reviewed and the relation of equivalence testing and distance-based
inference is highlighted. A new distance-based method to determine whether two gene lists are equivalent in terms of their annotations in the Gene Ontology illustrates these ideas. We end with a general discussion and some suggestions for future research.Tue, 01 Jan 2008 00:00:00 GMThttp://hdl.handle.net/2099/89352008-01-01T00:00:00ZOcaña i Rebull, Jordi; Sánchez Olavarría, María Pilar; Sánchez, Alex; Carrasco Jordan, Josep LluísnoCrossover designs, TOST, Intersection-union, Distance-based inference, Validation of simulation models, Gene OntologyEquivalence testing is the natural approach to many statistical problems. First, its main application, bioequivalence testing, is reviewed. The basic concepts of bioequivalence testing (2×2 crossover
designs, TOST, interval inclusion principle, etc.) and its problems (TOST biased character, the carryover problem, etc.) are considered. Next, equivalence testing is discussed more generally. Some applications and methods are reviewed and the relation of equivalence testing and distance-based
inference is highlighted. A new distance-based method to determine whether two gene lists are equivalent in terms of their annotations in the Gene Ontology illustrates these ideas. We end with a general discussion and some suggestions for future research.A microbiology application of the skew-Laplace distribution
http://hdl.handle.net/2099/8933
Title: A microbiology application of the skew-Laplace distribution
Authors: Julià, Olga; Vives Rego, Josep
Abstract: Flow cytometry scatter are ofen used in microbiology, and their measures are related to bacteria size and granularity. We present an application of the skew-Laplace distribution to flow cytometry data. The
goodness of fit is evaluated both graphically and numerically. We also study skewness and kurtosis values to assess usefulness of the skew-Laplace distribution.Tue, 01 Jan 2008 00:00:00 GMThttp://hdl.handle.net/2099/89332008-01-01T00:00:00ZJulià, Olga; Vives Rego, JosepnoSkew-Laplace distribution, Goodness of fit, Bacteria sizeFlow cytometry scatter are ofen used in microbiology, and their measures are related to bacteria size and granularity. We present an application of the skew-Laplace distribution to flow cytometry data. The
goodness of fit is evaluated both graphically and numerically. We also study skewness and kurtosis values to assess usefulness of the skew-Laplace distribution.Assessing influence in survival data with a cure fraction and covariates
http://hdl.handle.net/2099/8932
Title: Assessing influence in survival data with a cure fraction and covariates
Authors: Ortega, Edwin M. M.; Cancho, Vicente G.; Lachos, Victor Hugo
Abstract: Diagnostic methods have been an important tool in regression analysis to detect anomalies, such as departures from error assumptions and the presence of outliers and influential observations with the fitted models. Assuming censored data, we considered a classical analysis and Bayesian analysis assuming no informative priors for the parameters of the model with a cure fraction. A Bayesian approach was considered by using Markov Chain Monte Carlo Methods with Metropolis-Hasting algorithms steps to obtain the posterior summaries of interest. Some influence methods, such as the local influence, total local influence of an individual, local influence on predictions and generalized leverage were derived, analyzed and discussed in survival data with a cure fraction and covariates. The relevance of the approach was illustrated with a real data set, where it is shown that, by removing the most influential observations, the decision about which model best fits the data is changed.Tue, 01 Jan 2008 00:00:00 GMThttp://hdl.handle.net/2099/89322008-01-01T00:00:00ZOrtega, Edwin M. M.; Cancho, Vicente G.; Lachos, Victor HugonoCure fraction, Bayesian inference, Local influence, Generalized leverage, Survival data.Diagnostic methods have been an important tool in regression analysis to detect anomalies, such as departures from error assumptions and the presence of outliers and influential observations with the fitted models. Assuming censored data, we considered a classical analysis and Bayesian analysis assuming no informative priors for the parameters of the model with a cure fraction. A Bayesian approach was considered by using Markov Chain Monte Carlo Methods with Metropolis-Hasting algorithms steps to obtain the posterior summaries of interest. Some influence methods, such as the local influence, total local influence of an individual, local influence on predictions and generalized leverage were derived, analyzed and discussed in survival data with a cure fraction and covariates. The relevance of the approach was illustrated with a real data set, where it is shown that, by removing the most influential observations, the decision about which model best fits the data is changed.