Robust Location and Scale Estimation Based on the Univariate Generalized<i>t</i>(<i>GT</i>) Distribution

Arslan, Olçay; Genç, Alі İ.

doi:10.1081/sta-120022242

Cited by 22 publications

(16 citation statements)

References 16 publications

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“…For the general case, see, e.g., [24], and for the particular case of the multivariate skew-t distribution, see [10]. In [5], a scale mixture expression of exponential power distributions is given for a generalized-t distribution de ned in [23]. A general scale mixture of p-generalized normal distributions is dealt with in Section 3.3 in [4] including the special case of p-generalized Student-t distribution.…”

Section: Preliminariesmentioning

confidence: 99%

Extreme value distributions for dependent jointly l_n,p-symmetrically distributed random variables

Müller

Richter

2016

Dependence Modeling

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A measure-of-cone representation of skewed continuous ln,p-symmetric distributions, n ∈ N, p > , is proved following the geometric approach known for elliptically contoured distributions. On this basis, distributions of extreme values of n dependent random variables are derived if the latter follow a joint continuous ln,p-symmetric distribution. Light, heavy, and extremely far tails as well as tail indices are discussed, and new parameters of multivariate tail behavior are introduced.

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Section: Preliminariesmentioning

confidence: 99%

Extreme value distributions for dependent jointly l_n,p-symmetrically distributed random variables

Müller

Richter

2016

Dependence Modeling

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“…This distribution was used by for McDonald and Newey [15] for partially adaptive estimation of regression models. It has been followed up more recently by Theodossiou [16] and Arslan and Genc [17]. The corresponding cdf can be expressed as:…”

Section: Skew Generalized T Distributionmentioning

confidence: 99%

Skew Models II

Nadarajah

Kotz

2007

Acta Appl Math

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“…If we condition on 1 , 2 , … , , the conditional log-likelihood function will be = ∑ ( | 1 , 2 , … , − , ). = +1 (6) Consider the regression model given in (3). If it is assumed that 's are normally distributed the conditional log-likelihood function will be as follows (Alpuim and El-Shaarawi [2]).…”

Section: Conditional Likelihood Under Normalitymentioning

confidence: 99%

“…Consider the regression model given in equation (3) and assume that 's have t distribution with the density function…”

Section: Parameters Estimation Under T Distributionmentioning

confidence: 99%

Robust parameter estimation of regression model with AR(p) error terms

Tuaç

Güney

Şenoğlu

et al. 2017

Communications in Statistics - Simulation and Computation

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In this paper, we consider a linear regression model with AR(p) error terms with the assumption that the error terms have a t distribution as a heavy tailed alternative to the normal distribution. We obtain the estimators for the model parameters by using the conditional maximum likelihood (CML) method. We conduct an iteratively reweighting algorithm (IRA) to find the estimates for the parameters of interest. We provide a simulation study and three real data examples to illustrate the performance of the proposed robust estimators based on t distribution.

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Robust Location and Scale Estimation Based on the Univariate Generalizedt(GT) Distribution

Cited by 22 publications

References 16 publications

Extreme value distributions for dependent jointly l_n,p-symmetrically distributed random variables

Extreme value distributions for dependent jointly l_n,p-symmetrically distributed random variables

Skew Models II

Robust parameter estimation of regression model with AR(p) error terms

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Robust Location and Scale Estimation Based on the Univariate Generalizedt(GT) Distribution

Cited by 22 publications

References 16 publications

Extreme value distributions for dependent jointly ln,p-symmetrically distributed random variables

Extreme value distributions for dependent jointly ln,p-symmetrically distributed random variables

Skew Models II

Robust parameter estimation of regression model with AR(p) error terms

Contact Info

Product

Resources

About

Extreme value distributions for dependent jointly l_n,p-symmetrically distributed random variables

Extreme value distributions for dependent jointly l_n,p-symmetrically distributed random variables