12 research outputs found

    Analysis of panel data models with grouped observations

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    We present an iterative estimation procedure to estimate panel data models when some observations are missed or grouped with arbitrary classification intervals. The analysis is carried out from the perspective of panel data models, in which the error terms may follow an arbitrary distribution. We propose an easy-to-implement algorithm to estimate all of the model parameters and the asymptotic stochastic properties of the resulting estimate are investigated as the number of individuals and the number of time periods increase

    Analysis of panel data models with grouped observations

    Get PDF
    We present an iterative estimation procedure to estimate panel data models when some observations are missed or grouped with arbitrary classification intervals. The analysis is carried out from the perspective of panel data models, in which the error terms may follow an arbitrary distribution. We propose an easy-to-implement algorithm to estimate all of the model parameters and the asymptotic stochastic properties of the resulting estimate are investigated as the number of individuals and the number of time periods increase

    Dynamic mixed models for familial longitudinal data [book review]

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    SUTRADHAR, Brajendra C., "Dynamic mixed models for familial longitudinal data". Springer, 2011. ISBN 9781441983411Depto. de Estadística e Investigación OperativaFac. de Ciencias Matemáticaspu

    Robust analysis of variance with imprecise data: an ad hoc algorithm

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    We present an easy to implement algorithm, which is valid to analyse the variance of data under several robust conditions. Firstly, the observations may be precise or imprecise. Secondly, the error distributions may vary within the wide class of the strongly unimodal distributions, symmetrical or not. Thirdly, the variance of the errors is unknown. The algorithm starts by estimating the parameters of the ANOVA linear model. Then, the asymptotic covariance matrix of the effects is estimated. Finally, the algorithm uses this matrix estimate to test ANOVA hypotheses posed in terms of linear combinations of the effects

    Regularization in statistics - Discussion

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    Depto. de Estadística e Investigación OperativaFac. de Ciencias MatemáticasTRUEpu

    A robust algorithm for the sequential linear analysis of environmental radiological data with imprecise observations

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    In this paper we present an algorithm suitable to analyse linear models under the following robust conditions: the data is not received in batch but sequentially; the dependent variables may be either non-grouped or grouped, that is, imprecisely observed; the distribution of the errors may be general, thus, not necessarily normal; and the variance of the errors is unknown. As a consequence of the sequential data reception, the algorithm focuses on updating the current estimation and inference of the model parameters (slopes and error variance) as soon as a new data is received. The update of the current estimate is simple and needs scanty computational requirements. The same occurs with the inference processes which are based on asymptotics. The algorithm, unlike its natural competitors, has some memory; therefore, the storage of the complete up-to-date data set is not needed. This fact is essential in terms of computer complexity, so reducing both the computing time and storage requirements of our algorithm compared with other alternatives

    Mean-based iterative procedures in linear models with general errors and grouped data

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    We present in this paper iterative estimation procedures, using conditional expectations, to fit linear models when the distributions of the errors are general and the dependent data stem from a finite number of sources, either grouped or non-grouped with different classification criteria. We propose an initial procedure that is inspired by the expectation-maximization (EM) algorithm, although it does not agree with it. The proposed procedure avoids the nested iteration, which implicitly appears in the initial procedure and also in the EM algorithm. The stochastic asymptotic properties of the corresponding estimators are analysed

    Censores regression models with double exponential error distributions: an iterative estimation procedure based on medians for correcting bias

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    In this paper, we consider a simple iterative estimation procedure for censored regression models with symmetrical exponential error distributions. Although each step requires to impute the censored data with conditional medians, its tractability is guaranteed as well as its convergence at geometrical rate. Finally, as the final estimate coincides with a Huber M-estimator, its consistency and asymptotic normality are easily proved

    An algorithm for robust linear estimation with grouped data

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    An algorithm which is valid to estimate the parameters of linear models under several robust conditions is presented. With respect to the robust conditions, firstly, the dependent variables may be either non-grouped or grouped. Secondly, the distribution of the errors may vary within the wide class of the strongly unimodal distributions, either symmetrical or non-symmetrical. Finally, the variance of the errors is unknown. Under these circumstances the algorithm is not only capable of estimating the parameters (slopes and error variance) of the linear model, but also the asymptotic covariance matrix of the linear parameters. This opens the possibility of making inferences in terms of either multiple confidence regions or hypothesis testin

    An algorithm based on discrete response regression models suitable to correct the bias of non-response in surveys with several capture tries

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    The use of survey plans, which contemplate several tries or call-backs when endeavouring to capture individual data, may supply unarguable information in certain sampling situations with non-ignorable non-response. This paper presents an algorithm whose final aim is the estimation of the individual non-response probabilities from a general perspective of discrete response regression models, which includes the well known probit and logit models. It will be assumed that the respondents supply all the variables of interest when they are captured. Nevertheless, the call-backs continue. even after previous captures, for a small number of tries, r, which has been fixed beforehand only for estimating purposes. The different retries or call-backs are supposed to be carried out with different capture intensities. As mentioned above. the response probabilities, which may vary from one individual to another, are sought by discrete response regression models, whose parameters are estimated from conditioned likelihoods evaluated on the respondents only. The algorithm, quick and easy to implement, may be used even when the capture indicator matrix has been partially recorded. Finally, the practical performance of the proposed procedure is tested and evaluated from empirical simulations whose results are undoubtedly encouraging
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