2004, Vol. 28, Núm. 2
http://hdl.handle.net/2099/3722
2023-02-01T20:28:25ZBook review: Robert, C. P.; Casella, G. "Monte Carlo statistical methods" (2nd. ed.). New York: Springer, 2004, 645 pp + XXX, 132 illustrations, hardcover. Price: EUR 89.95
http://hdl.handle.net/2099/3781
Book review: Robert, C. P.; Casella, G. "Monte Carlo statistical methods" (2nd. ed.). New York: Springer, 2004, 645 pp + XXX, 132 illustrations, hardcover. Price: EUR 89.95
Moreno Fernández, Mario Victor; Iniesta, Raquel
This is the revised second edition of a textbook on statistical methods based on simulation, particularly those based on Markov Chains.
2007-11-14T19:19:57ZMoreno Fernández, Mario VictorIniesta, RaquelThis is the revised second edition of a textbook on statistical methods based on simulation, particularly those based on Markov Chains.Book review: Dale, Andrew I. "Most honourable remembrance. The life and the work of Thomas Bayes". New York: Springer-Verlag, 2003, 668 pp. with 29 illustrations
http://hdl.handle.net/2099/3780
Book review: Dale, Andrew I. "Most honourable remembrance. The life and the work of Thomas Bayes". New York: Springer-Verlag, 2003, 668 pp. with 29 illustrations
Puig, Pere
The book is very erudite and reflects the wide experience and knowledge of the author not only concerning the history of science but the social history of Europe during XVIII century. The book is appropriate for statisticians ans mathematicians, and also for students with interest in history os science.
2007-11-14T19:16:15ZPuig, PereThe book is very erudite and reflects the wide experience and knowledge of the author not only concerning the history of science but the social history of Europe during XVIII century. The book is appropriate for statisticians ans mathematicians, and also for students with interest in history os science.Asymptotically optimal filtering in linear systems with fractional Brownian noises
http://hdl.handle.net/2099/3776
Asymptotically optimal filtering in linear systems with fractional Brownian noises
Le Breton, Alain; Kleptsyna, Marina L.; Viot, Michel
In this paper, the filtering problem is revisited in the basic Gaussian homogeneous linear system driven by fractional Brownian motions. We exhibit a simple approximate filter which is asymptotically optimal
in the sense that, when the observation time tends to infinity, the variance of the corresponding filtering error converges to the same limit as for the exact optimal filter.
2007-11-14T17:29:07ZLe Breton, AlainKleptsyna, Marina L.Viot, MichelIn this paper, the filtering problem is revisited in the basic Gaussian homogeneous linear system driven by fractional Brownian motions. We exhibit a simple approximate filter which is asymptotically optimal
in the sense that, when the observation time tends to infinity, the variance of the corresponding filtering error converges to the same limit as for the exact optimal filter.On invariant density estimation for ergodic diffusion processes
http://hdl.handle.net/2099/3754
On invariant density estimation for ergodic diffusion processes
Kutoyants, Yu. A.
We present a review of several results concerning invariant density estimation by observations of ergodic diffusion process and some related problems. In every problem we propose a lower minimax bound on the risks of all estimators and then we construct an asymptotically efficient estimator.
2007-11-12T19:26:58ZKutoyants, Yu. A.We present a review of several results concerning invariant density estimation by observations of ergodic diffusion process and some related problems. In every problem we propose a lower minimax bound on the risks of all estimators and then we construct an asymptotically efficient estimator.Robust estimation and forecasting for beta-mixed hierarchical models of grouped binary data
http://hdl.handle.net/2099/3753
Robust estimation and forecasting for beta-mixed hierarchical models of grouped binary data
Pashkevich, Maxim A.; Kharin, Yurij S.
The paper focuses on robust estimation and forecasting techniques for grouped binary data with misclassified responses. It is assumed that the data are described by the beta-mixed hierarchical model (the beta-binomial or the beta-logistic), while the misclassifications are caused by the
stochastic additive distortions of binary observations. For these models, the effect of ignoring the misclassifications is evaluated and expressions for the biases of the method-of-moments estimators and maximum likelihood estimators, as well as expressions for the increase in the mean square error of forecasting for the Bayes predictor are given. To compensate the misclassification effects, new
consistent estimators and a new Bayes predictor, which take into account the distortion model, are constructed. The robustness of the developed techniques is demonstrated via computer simulations
and a real-life case study.
2007-11-12T19:25:15ZPashkevich, Maxim A.Kharin, Yurij S.The paper focuses on robust estimation and forecasting techniques for grouped binary data with misclassified responses. It is assumed that the data are described by the beta-mixed hierarchical model (the beta-binomial or the beta-logistic), while the misclassifications are caused by the
stochastic additive distortions of binary observations. For these models, the effect of ignoring the misclassifications is evaluated and expressions for the biases of the method-of-moments estimators and maximum likelihood estimators, as well as expressions for the increase in the mean square error of forecasting for the Bayes predictor are given. To compensate the misclassification effects, new
consistent estimators and a new Bayes predictor, which take into account the distortion model, are constructed. The robustness of the developed techniques is demonstrated via computer simulations
and a real-life case study.Extremes of periodic moving averages of random variables with regularly varying tail probabilities
http://hdl.handle.net/2099/3752
Extremes of periodic moving averages of random variables with regularly varying tail probabilities
Martins, Ana Paula; Ferreira, Helena
We define a family of local mixing conditions that enable the computation of the extremal index of periodic sequences from the joint distributions of kconsecutive variables of the sequence. By applying
results, under local and global mixing conditions, to the ( 2m – 1)–dependent periodic sequence X(m) n = Pm – 1
j = –m cj Zn – j, n ≥ 1, we compute the extremal index of the periodic moving average sequence Xn= P∞ j=–∞ cj Zn – j, n ≥ 1, of random variables with regularly varying tail probabilities.
This paper generalizes the theory for extremes of stationary moving averages with regularly varying tail probabilities.
2007-11-12T19:23:26ZMartins, Ana PaulaFerreira, HelenaWe define a family of local mixing conditions that enable the computation of the extremal index of periodic sequences from the joint distributions of kconsecutive variables of the sequence. By applying
results, under local and global mixing conditions, to the ( 2m – 1)–dependent periodic sequence X(m) n = Pm – 1
j = –m cj Zn – j, n ≥ 1, we compute the extremal index of the periodic moving average sequence Xn= P∞ j=–∞ cj Zn – j, n ≥ 1, of random variables with regularly varying tail probabilities.
This paper generalizes the theory for extremes of stationary moving averages with regularly varying tail probabilities.Estimation of the noncentrality matrix of a noncentral Wishart distribution with unit scale matrix. A matrix generalitzation of Lenng's domination result
http://hdl.handle.net/2099/3751
Estimation of the noncentrality matrix of a noncentral Wishart distribution with unit scale matrix. A matrix generalitzation of Lenng's domination result
Neudecker, Heinz
The main aim is to estimate the noncentrality matrix of a noncentral Wishart distribution. The method used is Leung’s but generalized to a matrix loss function. Parallelly Leung’s scalar noncentral Wishart
identity is generalized to become a matrix identity. The concept of L¨owner partial ordering of symmetric matrices is used.
2007-11-12T19:20:36ZNeudecker, HeinzThe main aim is to estimate the noncentrality matrix of a noncentral Wishart distribution. The method used is Leung’s but generalized to a matrix loss function. Parallelly Leung’s scalar noncentral Wishart
identity is generalized to become a matrix identity. The concept of L¨owner partial ordering of symmetric matrices is used.Some discrete exponential dispersion models: Poisson-Tweedie and Hinde-Demétrio classes
http://hdl.handle.net/2099/3750
Some discrete exponential dispersion models: Poisson-Tweedie and Hinde-Demétrio classes
Kokonendji, Célestin C.; Dossou-Gbété, Simplice; Demétrio, Clarice G.B.
In this paper we investigate two classes of exponential dispersion models (EDMs) for overdispersed count data with respect to the Poisson distribution. The first is a class of Poisson mixture with positive Tweedie mixing distributions. As an approximation (in terms of unit variance function) of the first, the second is a new class of EDMs characterized by their unit variance functions of the form µ + µp, where p
is a real index related to a precise model. These two classes provide some alternatives to the negative binomial distribution ( p= 2) which is classically used in the framework of regression models for count
data when overdispersion results in a lack of fit of the Poisson regression model. Some properties are then studied and the practical usefulness is also discussed.
2007-11-12T19:18:21ZKokonendji, Célestin C.Dossou-Gbété, SimpliceDemétrio, Clarice G.B.In this paper we investigate two classes of exponential dispersion models (EDMs) for overdispersed count data with respect to the Poisson distribution. The first is a class of Poisson mixture with positive Tweedie mixing distributions. As an approximation (in terms of unit variance function) of the first, the second is a new class of EDMs characterized by their unit variance functions of the form µ + µp, where p
is a real index related to a precise model. These two classes provide some alternatives to the negative binomial distribution ( p= 2) which is classically used in the framework of regression models for count
data when overdispersion results in a lack of fit of the Poisson regression model. Some properties are then studied and the practical usefulness is also discussed.A comparative study of small area estimators
http://hdl.handle.net/2099/3749
A comparative study of small area estimators
Molina, Isabel; Santamaría Arana, Laureano; Morales González, Domingo
It is known that direct-survey estimators of small area parameters, calculated with the data from the given small area, often present large mean squared errors because of small sample sizes in the small areas. Model–based estimators borrow strength from other related areas to avoid this problem. How
small should domain sample sizes be to recommend the use of model-based estimators? How robust small area estimators are with respect to the rate sample size/number of domains? To give answers or recommendations about the questions above, a Monte Carlo simulation experiment is carried out. In this simulation study, model-based estimators for small areas are compared with some standard design-based estimators. The simulation study starts with the construction of an artificial population data file, imitating a census file of an Statistical Office. A stratified random design is used to draw samples from the artificial population. Small area estimators of the mean of a continuous variable are calculated for all small areas and compared by using different performance measures. The
evolution of this performance measures is studied when increasing the number of small areas, which means to decrease their sizes.
2007-11-12T19:16:20ZMolina, IsabelSantamaría Arana, LaureanoMorales González, DomingoIt is known that direct-survey estimators of small area parameters, calculated with the data from the given small area, often present large mean squared errors because of small sample sizes in the small areas. Model–based estimators borrow strength from other related areas to avoid this problem. How
small should domain sample sizes be to recommend the use of model-based estimators? How robust small area estimators are with respect to the rate sample size/number of domains? To give answers or recommendations about the questions above, a Monte Carlo simulation experiment is carried out. In this simulation study, model-based estimators for small areas are compared with some standard design-based estimators. The simulation study starts with the construction of an artificial population data file, imitating a census file of an Statistical Office. A stratified random design is used to draw samples from the artificial population. Small area estimators of the mean of a continuous variable are calculated for all small areas and compared by using different performance measures. The
evolution of this performance measures is studied when increasing the number of small areas, which means to decrease their sizes.