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1.
In this paper we introduce a new extension for the Birnbaum–Saunder distribution based on the family of the epsilon-skew-symmetric
distributions studied in Arellano-Valle et al. (J Stat Plan Inference 128(2):427–443, 2005). The extension allows generating
Birnbaun–Saunders type distributions able to deal with extreme or outlying observations (Dupuis and Mills, IEEE Trans Reliab
47:88–95, 1998). Basic properties such as moments and Fisher information matrix are also studied. Results of a real data application
are reported illustrating good fitting properties of the proposed model. 相似文献
2.
Scale mixtures of normal distributions form a class of symmetric thick-tailed distributions that includes the normal one as
a special case. In this paper we consider local influence analysis for measurement error models (MEM) when the random error
and the unobserved value of the covariates jointly follow scale mixtures of normal distributions, providing an appealing robust
alternative to the usual Gaussian process in measurement error models. In order to avoid difficulties in estimating the parameter
of the mixing variable, we fixed it previously, as recommended by Lange et al. (J Am Stat Assoc 84:881–896, 1989) and Berkane
et al. (Comput Stat Data Anal 18:255–267, 1994). The local influence method is used to assess the robustness aspects of the
parameter estimates under some usual perturbation schemes. However, as the observed log-likelihood associated with this model
involves some integrals, Cook’s well–known approach may be hard to apply to obtain measures of local influence. Instead, we
develop local influence measures following the approach of Zhu and Lee (J R Stat Soc Ser B 63:121–126, 2001), which is based
on the EM algorithm. Results obtained from a real data set are reported, illustrating the usefulness of the proposed methodology,
its relative simplicity, adaptability and practical usage. 相似文献
3.
In this paper, we introduce an alternative stochastic restricted Liu estimator for the vector of parameters in a linear regression
model when additional stochastic linear restrictions on the parameter vector are assumed to hold. The new estimator is a generalization
of the ordinary mixed estimator (OME) (Durbin in J Am Stat Assoc 48:799–808, 1953; Theil and Goldberger in Int Econ Rev 2:65–78,
1961; Theil in J Am Stat Assoc 58:401–414, 1963) and Liu estimator proposed by Liu (Commun Stat Theory Methods 22:393–402,
1993). Necessary and sufficient conditions for the superiority of the new stochastic restricted Liu estimator over the OME,
the Liu estimator and the estimator proposed by Hubert and Wijekoon (Stat Pap 47:471–479, 2006) in the mean squared error
matrix (MSEM) sense are derived. Furthermore, a numerical example based on the widely analysed dataset on Portland cement
(Woods et al. in Ind Eng Chem 24:1207–1241, 1932) and a Monte Carlo evaluation of the estimators are also given to illustrate
some of the theoretical results. 相似文献
4.
This paper considers the problem of hypothesis testing in a simple panel data regression model with random individual effects
and serially correlated disturbances. Following Baltagi et al. (Econom. J. 11:554–572, 2008), we allow for the possibility of non-stationarity in the regressor and/or the disturbance term. While Baltagi et al. (Econom.
J. 11:554–572, 2008) focus on the asymptotic properties and distributions of the standard panel data estimators, this paper focuses on testing
of hypotheses in this setting. One important finding is that unlike the time-series case, one does not necessarily need to
rely on the “super-efficient” type AR estimator by Perron and Yabu (J. Econom. 151:56–69, 2009) to make an inference in the panel data. In fact, we show that the simple t-ratio always converges to the standard normal distribution, regardless of whether the disturbances and/or the regressor are
stationary. 相似文献
5.
We introduce a new family of skew-normal distributions that contains the skew-normal distributions introduced by Azzalini
(Scand J Stat 12:171–178, 1985), Arellano-Valle et al. (Commun Stat Theory Methods 33(7):1465–1480, 2004), Gupta and Gupta
(Test 13(2):501–524, 2008) and Sharafi and Behboodian (Stat Papers, 49:769–778, 2008). We denote this distribution by GBSN
n
(λ1, λ2). We present some properties of GBSN
n
(λ1, λ2) and derive the moment generating function. Finally, we use two numerical examples to illustrate the practical usefulness
of this distribution. 相似文献
6.
Kajsa Kvist Per Kragh Andersen Jules Angst Lars Vedel Kessing 《Lifetime data analysis》2010,16(4):580-598
The effect of event-dependent sampling of processes consisting of recurrent events is investigated when analyzing whether
the risk of recurrence increases with event count. We study the situation where processes are selected for study if an event
occurs in a certain selection interval. Motivation comes from psychiatric epidemiology where repeated hospital admissions
are studied for patients with affective disease, as seen in Kessing et al. (Acta Psychiatr Scand 109:339–344, 2004b). For
the selected processes, either only disease course from selection and onwards is used in the analysis, or, both retrospective
and prospective disease course histories are used. We examine two methods to correct for the selection depending on which
data are used in the analysis. In the first case, the conditional distribution of the process given the pre-selection history
is determined. In the second case, an inverse-probability-of-selection weighting scheme is suggested. The ability of the methods
to correct for the bias due to selection is investigated with simulations. Furthermore, the methods are applied to affective
disease data from a register-based study (Kessing et al. Br J Psychiatry 185:372–377, 2004a) and from a long-term clinical
study (Kessing et al. Acta Psychiatr Scand 109:339–344, 2004b). 相似文献
7.
Robinson Kruse 《Statistical Papers》2011,52(1):71-85
This paper proposes a new unit root test against a nonlinear exponential smooth transition autoregressive model. This model
receives much attention in international macroeconomics as it has been successfully applied to a variety of financial time
series. The new test is build upon the nonstandard testing approach of Abadir and Distaso (J Econom 140:695–718, 2007) who
introduce a class of modified statistics for testing joint hypotheses when one of the alternatives is one-sided. The asymptotic
properties of the suggested unit root test are derived. In a Monte Carlo study the popular Dickey–Fuller-type test proposed
by Kapetanios et al. (J Econom 112:359–379, 2003) is compared to the new test. The results suggest that the new test is generally
superior in terms of power. An application to a real effective exchange rate underlines its usefulness. 相似文献
8.
Sasabuchi et al. (Biometrika 70(2):465–472, 1983) introduces a multivariate version of the well-known univariate isotonic
regression which plays a key role in the field of statistical inference under order restrictions. His proposed algorithm for
computing the multivariate isotonic regression, however, is guaranteed to converge only under special conditions (Sasabuchi
et al., J Stat Comput Simul 73(9):619–641, 2003). In this paper, a more general framework for multivariate isotonic regression
is given and an algorithm based on Dykstra’s method is used to compute the multivariate isotonic regression. Two numerical
examples are given to illustrate the algorithm and to compare the result with the one published by Fernando and Kulatunga
(Comput Stat Data Anal 52:702–712, 2007). 相似文献
9.
Angelika van der Linde 《AStA Advances in Statistical Analysis》2009,93(3):307-333
Recently, van der Linde (Comput. Stat. Data Anal. 53:517–533, 2008) proposed a variational algorithm to obtain approximate Bayesian inference in functional principal components analysis (FPCA),
where the functions were observed with Gaussian noise. Generalized FPCA under different noise models with sparse longitudinal
data was developed by Hall et al. (J. R. Stat. Soc. B 70:703–723, 2008), but no Bayesian approach is available yet. It is demonstrated that an adapted version of the variational algorithm can
be applied to obtain a Bayesian FPCA for canonical parameter functions, particularly log-intensity functions given Poisson
count data or logit-probability functions given binary observations. To this end a second order Taylor expansion of the log-likelihood,
that is, a working Gaussian distribution and hence another step of approximation, is used. Although the approach is conceptually
straightforward, difficulties can arise in practical applications depending on the accuracy of the approximation and the information
in the data. A modified algorithm is introduced generally for one-parameter exponential families and exemplified for binary
and count data. Conditions for its successful application are discussed and illustrated using simulated data sets. Also an
application with real data is presented. 相似文献
10.
Modelling count data with overdispersion and spatial effects 总被引:1,自引:1,他引:0
In this paper we consider regression models for count data allowing for overdispersion in a Bayesian framework. We account
for unobserved heterogeneity in the data in two ways. On the one hand, we consider more flexible models than a common Poisson
model allowing for overdispersion in different ways. In particular, the negative binomial and the generalized Poisson (GP)
distribution are addressed where overdispersion is modelled by an additional model parameter. Further, zero-inflated models
in which overdispersion is assumed to be caused by an excessive number of zeros are discussed. On the other hand, extra spatial
variability in the data is taken into account by adding correlated spatial random effects to the models. This approach allows
for an underlying spatial dependency structure which is modelled using a conditional autoregressive prior based on Pettitt
et al. in Stat Comput 12(4):353–367, (2002). In an application the presented models are used to analyse the number of invasive
meningococcal disease cases in Germany in the year 2004. Models are compared according to the deviance information criterion
(DIC) suggested by Spiegelhalter et al. in J R Stat Soc B64(4):583–640, (2002) and using proper scoring rules, see for example
Gneiting and Raftery in Technical Report no. 463, University of Washington, (2004). We observe a rather high degree of overdispersion
in the data which is captured best by the GP model when spatial effects are neglected. While the addition of spatial effects
to the models allowing for overdispersion gives no or only little improvement, spatial Poisson models with spatially correlated
or uncorrelated random effects are to be preferred over all other models according to the considered criteria. 相似文献
11.
The Fisher information matrix for a mixture of two Laplace distributions is derived. Numerical tabulations of the matrix and
a computer program are provided for practical purposes. The work is motivated by two real–life examples discussed in Hsu (Appl
Stat 28:62–72, 1979) and Bhowmick et al. (Biostatistics 7:630–641, 2006).
相似文献
12.
Recurrent event data occur in many clinical and observational studies (Cook and Lawless, Analysis of recurrent event data,
2007) and in these situations, there may exist a terminal event such as death that is related to the recurrent event of interest
(Ghosh and Lin, Biometrics 56:554–562, 2000; Wang et al., J Am Stat Assoc 96:1057–1065, 2001; Huang and Wang, J Am Stat Assoc
99:1153–1165, 2004; Ye et al., Biometrics 63:78–87, 2007). In addition, sometimes there may exist more than one type of recurrent
events, that is, one faces multivariate recurrent event data with some dependent terminal event (Chen and Cook, Biostatistics
5:129–143, 2004). It is apparent that for the analysis of such data, one has to take into account the dependence both among
different types of recurrent events and between the recurrent and terminal events. In this paper, we propose a joint modeling
approach for regression analysis of the data and both finite and asymptotic properties of the resulting estimates of unknown
parameters are established. The methodology is applied to a set of bivariate recurrent event data arising from a study of
leukemia patients. 相似文献
13.
In this paper we have discussed inference aspects of the skew-normal nonlinear regression models following both, a classical
and Bayesian approach, extending the usual normal nonlinear regression models. The univariate skew-normal distribution that
will be used in this work was introduced by Sahu et al. (Can J Stat 29:129–150, 2003), which is attractive because estimation
of the skewness parameter does not present the same degree of difficulty as in the case with Azzalini (Scand J Stat 12:171–178,
1985) one and, moreover, it allows easy implementation of the EM-algorithm. As illustration of the proposed methodology, we
consider a data set previously analyzed in the literature under normality. 相似文献
14.
In this paper we discuss new adaptive proposal strategies for sequential Monte Carlo algorithms—also known as particle filters—relying
on criteria evaluating the quality of the proposed particles. The choice of the proposal distribution is a major concern and
can dramatically influence the quality of the estimates. Thus, we show how the long-used coefficient of variation (suggested
by Kong et al. in J. Am. Stat. Assoc. 89(278–288):590–599, 1994) of the weights can be used for estimating the chi-square distance between the target and instrumental distributions of the
auxiliary particle filter. As a by-product of this analysis we obtain an auxiliary adjustment multiplier weight type for which
this chi-square distance is minimal. Moreover, we establish an empirical estimate of linear complexity of the Kullback-Leibler
divergence between the involved distributions. Guided by these results, we discuss adaptive designing of the particle filter
proposal distribution and illustrate the methods on a numerical example.
This work was partly supported by the National Research Agency (ANR) under the program “ANR-05-BLAN-0299”. 相似文献
15.
Of interest is the analysis of data resulting from a series of experiments repeated at several environments with the same
set of plant varieties. Suppose that the experiments, multi-environment variety trials (as they are called), are all conducted
in resolvable incomplete block designs. Adopting the randomization-derived mixed model obtained in Caliński et al. (Biometrics
61:448–455, 2005), a suitable analysis of variance methodology is considered and relevant test procedures are examined. The
proposed methods are illustrated by the analysis of results of a series of trials with rye varieties. 相似文献
16.
This paper considers the implementation of a mean-reverting interest rate model with Markov-modulated parameters. Hidden Markov
model filtering techniques in Elliott (1994, Automatica, 30:1399–1408) and Elliott et al. (1995, Hidden Markov Models: Estimation
and Control. Springer, New York) are employed to obtain optimal estimates of the model parameters via recursive filters of
auxiliary quantities of the observation process. Algorithms are developed and implemented on a financial dataset of 30-day
Canadian Treasury bill yields. We also provide standard errors for the model parameter estimates. Our analysis shows that
within the dataset and period studied, a model with two regimes is sufficient to describe the interest rate dynamics on the
basis of very small prediction errors and the Akaike information criterion. 相似文献
17.
Soo Hak Sung 《Statistical Papers》2012,53(1):73-82
In this paper, we obtain a complete convergence result for weighted sums of negatively dependent random variables under mild
conditions of weights. This result generalizes and improves the result of Zarei and Jabbari (Stat Papers doi:, 2009). Our result also extends the result of Taylor et al. (Stoch Anal Appl 20:643–656, 2002) on unweighted average to a
weighted average. 相似文献
18.
The purpose of this paper is to develop a Bayesian analysis for the right-censored survival data when immune or cured individuals
may be present in the population from which the data is taken. In our approach the number of competing causes of the event
of interest follows the Conway–Maxwell–Poisson distribution which generalizes the Poisson distribution. Markov chain Monte
Carlo (MCMC) methods are used to develop a Bayesian procedure for the proposed model. Also, some discussions on the model
selection and an illustration with a real data set are considered. 相似文献
19.
In this paper, A variance decomposition approach to quantify the effects of endogenous and exogenous variables for nonlinear
time series models is developed. This decomposition is taken temporally with respect to the source of variation. The methodology
uses Monte Carlo methods to affect the variance decomposition using the ANOVA-like procedures proposed in Archer et al. (J.
Stat. Comput. Simul. 58:99–120, 1997), Sobol’ (Math. Model. 2:112–118, 1990). The results of this paper can be used in investment problems, biomathematics and control theory, where nonlinear time series
with multiple inputs are encountered. 相似文献
20.
On locally optimal invariant unbiased tests for the variance components ratio in mixed linear models
Andrzej Michalski 《Statistical Papers》2009,50(4):855-868
In the paper the problem of testing of two-sided hypotheses for variance components in mixed linear models is considered.
When the uniformly most powerful invariant test does not exist (see e.g. Das and Sinha, in Proceedings of the second international
Tampere conference in statistics, 1987; Gnot and Michalski, in Statistics 25:213–223, 1994; Michalski and Zmyślony, in Statistics
27:297–310, 1996) then to conduct the optimal statistical inference on model parameters a construction of a test with locally
best properties is desirable, cf. Michalski (in Tatra Mountains Mathematical Publications 26:1–21, 2003). The main goal of
this article is the construction of the locally best invariant unbiased test for a single variance component (or for a ratio
of variance components). The result has been obtained utilizing Andersson’s and Wijsman’s approach connected with a representation
of density function of maximal invariant (Andersson, in Ann Stat 10:955–961, 1982; Wijsman, in Proceedings of fifth Berk Symp
Math Statist Prob 1:389–400, 1967; Wijsman, in Sankhyā A 48:1–42, 1986; Khuri et al., in Statistical tests for mixed linear models, 1998) and from generalized Neyman–Pearson Lemma
(Dantzig and Wald, in Ann Math Stat 22:87–93, 1951; Rao, in Linear statistical inference and its applications, 1973). One
selected real example of an unbalanced mixed linear model is given, for which the power functions of the LBIU test and Wald’s
test (the F-test in ANOVA model) are computed, and compared with the attainable upper bound of power obtained by using Neyman–Pearson
Lemma. 相似文献