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Showing 10 results for Quantile
Mohammad Hossein Aalamatsaz, Foroogh Mahpishanian,
Volume 5, Issue 1 (9-2011)
Abstract
There is a family of generalized Farlie-Gumbel-Morgenstern copulas, known as the semiparametric family, which is generated by a function called distribution-based generator. These generators have been studied typically for symmetric distributions in the literature. In this article, is proposed a method for asymmetric case which increases the flexibility of distribution-based generators and, thus, the model. In addition, a method for generalizing general generators is provided which can also be used to obtain more flexible distribution-based generators. Clearly, with more flexible generators more desirable models can be found to fit real data.
Elham Zamanzadeh, Jafar Ahmadi,
Volume 5, Issue 1 (9-2011)
Abstract
In this paper, first a brief introduction of ranked set sampling is presented. Then, construction of confidence intervals for a quantile of the parent distribution based on ordered ranked set sample is given. Because the corresponding confidence coefficient is an step function, one may not be able to find the exact prescribed value. With this in mind, we introduce a new method and show that one can obtained an optimal confidence interval by appealing the proposed approach. We also compare the proposed scheme with the other existence methods.
Ali Aghamohammadi, Sakineh Mohammadi,
Volume 9, Issue 2 (2-2016)
Abstract
In many medical studies, in order to describe the course of illness and treatment effects, longitudinal studies are used. In longitudinal studies, responses are measured frequently over time, but sometimes these responses are discrete and with two-state. Recently Binary quantile regression methods to analyze this kind of data have been taken into consideration. In this paper, quantile regression model with Lasso and adaptive Lasso penalty for longitudinal data with dichotomous responses is provided. Since in both methods posteriori distributions of the parameters are not in explicit form, thus the full conditional posteriori distributions of parameters are calculated and the Gibbs sampling algorithm is used to deduction. To compare the performance of the proposed methods with the conventional methods, a simulation study was conducted and at the end, applications to a real data set are illustrated.
Fatemeh Hooti, Jafar Ahmadi,
Volume 10, Issue 1 (8-2016)
Abstract
In this paper, the quantile function is recalled and some reliability measures are rewritten in terms of quantile function. Next, quantile based dynamic cumulative residual entropy is obtained and some of its properties are presented. Then, some characterization results of uniform, exponential and Pareto distributions based on quantile based dynamic cumulative entropy are provided. A simple estimator is also proposed and its performance is studied for exponential distribution. Finally discussion and results are presented.
Ali Aghamohammadi, Mahdi Sojoudi,
Volume 10, Issue 2 (2-2017)
Abstract
Value-at-Risk and Average Value-at-Risk are tow important risk measures based on statistical methoeds that used to measure the market's risk with quantity structure. Recently, linear regression models such as least squares and quantile methods are introduced to estimate these risk measures. In this paper, these two risk measures are estimated by using omposite quantile regression. To evaluate the performance of the proposed model with the other models, a simulation study was conducted and at the end, applications to real data set from Iran's stock market are illustarted.
Afsaneh Shokrani, Mohammad Khorashadizadeh,
Volume 12, Issue 2 (3-2019)
Abstract
This paper first introduces the Kerridge inaccuracy measure as an extension of the Shannon entropy and then the measure of past inaccuracy has been rewritten based on the concept of quantile function. Then, some characterizations results for lifetimes with proportional reversed hazard model property based on quantile past inaccuracy measure are obtained. Also, the class of lifetimes with increasing (decreasing) quantile past inaccuracy property and some of its properties are studied. In addition, via an example of real data, the application of quantile inaccuracy measure is illustrated.
Vahideh Ahrari, Simindokht Baratpour, Arezo Habibirad,
Volume 12, Issue 2 (3-2019)
Abstract
Entropy plays a fundamental role in reliability and system lifetesting areas. In the recent studies, much attentions have been paid to use quantile functions properties and their applications as an alternate approac in distinguishing statistical models and analysis of data. In the present paper, quantile based residual Tsallis entropy is introduced and its properties in continuous models are investigated. Considering distributions of certain lifetime, explicit versions for quantile based residual Tsallis entropy are obtained and their properties monotonicity are studied and characterization based on this entropy is investigated. Also quantile based Tsallis divergence is introduced and quantile based residual Tsallis divergence is obtained. Finally, an estimator for the quantile based residual Tsallis entropy is introduced and its performance is investigate by study simulation.
Esmaeil Shirazi,
Volume 14, Issue 1 (8-2020)
Abstract
In this paper, we consider an adaptive wavelet estimation for quantile density function based on block thresholding method and obtain it's convergence rate under L2 loss function over Besove function spaces. This work is an extension of results in Chesneau et. al. (2016) and shows that the block threshold estimator gets better convergence rate (Optimal) than the estimators proposed by Chesneau et. al. (2016). The performance of the proposed estimator is investigated with a simulation study.
Miss Forouzan Jafari, Dr. Mousa Golalizadeh,
Volume 17, Issue 2 (2-2024)
Abstract
The mixed effects model is one of the powerful statistical approaches used to model the relationship between the response variable and some predictors in analyzing data with a hierarchical structure. The estimation of parameters in these models is often done following either the least squares error or maximum likelihood approaches. The estimated parameters obtained either through the least squares error or the maximum likelihood approaches are inefficient, while the error distributions are non-normal. In such cases, the mixed effects quantile regression can be used. Moreover, when the number of variables studied increases, the penalized mixed effects quantile regression is one of the best methods to gain prediction accuracy and the model's interpretability. In this paper, under the assumption of an asymmetric Laplace distribution for random effects, we proposed a double penalized model in which both the random and fixed effects are independently penalized. Then, the performance of this new method is evaluated in the simulation studies, and a discussion of the results is presented along with a comparison with some competing models. In addition, its application is demonstrated by analyzing a real example.
Dr Adeleh Fallah,
Volume 18, Issue 1 (8-2024)
Abstract
In this paper, non-parametric inference is considered for $k$-component coherent systems, when the system lifetime data is progressively type-II censored. In these coherent systems, it is assumed that the system structure and system signature are known. Based on the observed progressively type-II censored, non-parametric confidence intervals are calculated for the quantiles of component lifetime distribution. Also, tolerance limits for component lifetime distribution are obtained. Non-parametric confidence intervals for quantiles and tolerance limits are obtained based on two methods, distribution function method and W mixed matrix method. Two numerical example is used to illustrate the methodologies developed in this paper.
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