Minimum Density Power Divergence Estimation for Normal-Exponential Distribution
نویسندگان
چکیده
منابع مشابه
Minimum density power divergence estimator for diffusion processes
In this paper, we consider the robust estimation for a certain class of diffusion processes including the Ornstein–Uhlenbeck process based on discrete observations. As a robust estimator, we consider the minimum density power divergence estimator (MDPDE) proposed by Basu et al. (Biometrika 85:549–559, 1998). It is shown that the MDPDE is consistent and asymptotically normal. A simulation study ...
متن کاملRobust Estimation in Linear Regression Model: the Density Power Divergence Approach
The minimum density power divergence method provides a robust estimate in the face of a situation where the dataset includes a number of outlier data. In this study, we introduce and use a robust minimum density power divergence estimator to estimate the parameters of the linear regression model and then with some numerical examples of linear regression model, we show the robustness of this est...
متن کاملA note on the asymptotic distribution of the minimum density power divergence estimator
Basu et al. [1] and [2] introduce the minimum density power divergence estimator (MDPDE) as a parametric estimator that balances infinitesimal robustness and asymptotic efficiency. The MDPDE depends on a tuning constant α ≥ 0 that controls this trade-off. For α = 0 the MDPDE becomes the maximum likelihood estimator, which under certain regularity conditions is asymptotically efficient, see chap...
متن کاملComposite Likelihood Methods Based on Minimum Density Power Divergence Estimator
In this paper a robust version of the Wald test statistic for composite likelihood is 11 considered by using the composite minimum density power divergence estimator instead of the 12 composite maximum likelihood estimator. This new family of test statistics will be called Wald-type 13 test statistics. The problem of testing a simple and a composite null hypothesis is considered and 14 the robu...
متن کاملChannel Input Distribution Estimation Using a Minimum I-divergence Algorithm
Given a channel with a known transition probability, we consider the problem of finding the input distribution that most closely achieves a desired output distribution. We pose the problem as a linear inverse problem subject to nonnegativity constraints, and employ an iterative algorithm for minimizing Csiszar’s I-divergence between the desired channel output and the channel output derived from...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Korean Journal of Applied Statistics
سال: 2014
ISSN: 1225-066X
DOI: 10.5351/kjas.2014.27.3.397