2007, Vol. 31, Núm. 1http://hdl.handle.net/2099/37822024-08-08T05:15:13Z2024-08-08T05:15:13ZObjective Bayesian point and region estimation in location-scale modelsBernardo, José Miguelhttp://hdl.handle.net/2099/38072020-07-21T19:05:52Z2007-11-16T16:24:45ZObjective Bayesian point and region estimation in location-scale models
Bernardo, José Miguel
Point and region estimation may both be described as specific decision problems. In point estimation,the action space is the set of possible values of the quantity on interest; in region estimation, the action
space is the set of its possible credible regions. Foundations dictate that the solution to these decision problems must depend on both the utility function and the prior distribution. Estimators intended for
general use should surely be invariant under one-to-one transformations, and this requires the use of an invariant loss function; moreover, an objective solution requires the use of a prior which does
not introduce subjective elements. The combined use of an invariant information-theory based loss function, the intrinsic discrepancy, and an objective prior, the reference prior, produces a general
solution to both point and region estimation problems. In this paper, estimation of the two parameters of univariate location-scale models is considered in detail from this point of view, with special attention
to the normal model. The solutions found are compared with a range of conventional solutions.
2007-11-16T16:24:45ZBernardo, José MiguelPoint and region estimation may both be described as specific decision problems. In point estimation,the action space is the set of possible values of the quantity on interest; in region estimation, the action
space is the set of its possible credible regions. Foundations dictate that the solution to these decision problems must depend on both the utility function and the prior distribution. Estimators intended for
general use should surely be invariant under one-to-one transformations, and this requires the use of an invariant loss function; moreover, an objective solution requires the use of a prior which does
not introduce subjective elements. The combined use of an invariant information-theory based loss function, the intrinsic discrepancy, and an objective prior, the reference prior, produces a general
solution to both point and region estimation problems. In this paper, estimation of the two parameters of univariate location-scale models is considered in detail from this point of view, with special attention
to the normal model. The solutions found are compared with a range of conventional solutions.Goodness of fit tests for the skew-Laplace distributionPuig, PedroStephens, Michael A.http://hdl.handle.net/2099/38062020-07-21T19:05:53Z2007-11-16T16:17:49ZGoodness of fit tests for the skew-Laplace distribution
Puig, Pedro; Stephens, Michael A.
The skew-Laplace distribution is frequently used to fit the logarithm of particle sizes and it is also used in Economics, Engineering, Finance and Biology. We show the Anderson-Darling and Cram´ er-von
Mises goodness of fit tests for this distribution.
2007-11-16T16:17:49ZPuig, PedroStephens, Michael A.The skew-Laplace distribution is frequently used to fit the logarithm of particle sizes and it is also used in Economics, Engineering, Finance and Biology. We show the Anderson-Darling and Cram´ er-von
Mises goodness of fit tests for this distribution.Parameter estimation of S-distributions with alternating regressionChou, I-ChunMartens, HaraldVoit, Eberhard O.http://hdl.handle.net/2099/37962020-07-21T19:05:52Z2007-11-15T19:44:43ZParameter estimation of S-distributions with alternating regression
Chou, I-Chun; Martens, Harald; Voit, Eberhard O.
We propose a novel 3-way alternating regression (3-AR) method as an effective strategy for the estimation of parameter values in S-distributions from frequency data. The 3-AR algorithm is very
fast and performs well for error-free distributions and artificial noisy data obtained as random samples generated from S-distributions, as well as for traditional statistical distributions and for actual observation data. In rare cases where the algorithm does not immediately converge, its
enormous speed renders it feasible to select several initial guesses and search settings as an effective countermeasure.
2007-11-15T19:44:43ZChou, I-ChunMartens, HaraldVoit, Eberhard O.We propose a novel 3-way alternating regression (3-AR) method as an effective strategy for the estimation of parameter values in S-distributions from frequency data. The 3-AR algorithm is very
fast and performs well for error-free distributions and artificial noisy data obtained as random samples generated from S-distributions, as well as for traditional statistical distributions and for actual observation data. In rare cases where the algorithm does not immediately converge, its
enormous speed renders it feasible to select several initial guesses and search settings as an effective countermeasure.Nonparametric bivariate estimation for successive survival timesSerrat Piè, CarlesGómez, Guadalupehttp://hdl.handle.net/2099/37952020-07-21T19:05:53Z2007-11-15T19:40:25ZNonparametric bivariate estimation for successive survival times
Serrat Piè, Carles; Gómez, Guadalupe
Several aspects of the analysis of two successive survival times are considered. All the analyses take into account the dependent censoring on the second time induced by the first. Three nonparametric
methods are described, implemented and applied to the data coming from a multicentre clinical trial for HIV-infected patients. Visser’s and Wang and Wells methods propose an estimator for the bivariate survival function while G´omez and Serrat’s method presents a conditional approach for the second time given the first. The three approaches are compared and discussed at the end of the paper.
2007-11-15T19:40:25ZSerrat Piè, CarlesGómez, GuadalupeSeveral aspects of the analysis of two successive survival times are considered. All the analyses take into account the dependent censoring on the second time induced by the first. Three nonparametric
methods are described, implemented and applied to the data coming from a multicentre clinical trial for HIV-infected patients. Visser’s and Wang and Wells methods propose an estimator for the bivariate survival function while G´omez and Serrat’s method presents a conditional approach for the second time given the first. The three approaches are compared and discussed at the end of the paper.