Hazard rate and reliability function predictions for future observations

Date

2015-12-12

Author

Sürücü, Barış

Metadata

Show full item record

Item Usage Stats

99
views

0
downloads

Estimating reliability and hazard rate functions for various types of distributions based on sample information is an interesting problem in the statistical literature. The common and the simplest approach to this problem is to estimate the unknown parameters for the underlying distribution of concern and replace these unknown parameters in reliability and hazard rate functions by their estimators. However, this may lead to inconsistent coverage probabilities for the estimated functions. This classical approach may even perform much worse if one is interested in hazard rate and reliability function predictions for future observations. We will firstly introduce an efficient approximation algorithm for hazard rate and reliability functions, which achieves highly accurate coverage probabilities for their confidence intervals. We will also show that the convergence to asymptotic distributions for the functional estimators is quite fast. Secondly, we will discuss how predicted future order statistics of a specified distribution are obtained and utilized in this context. Results of a simulation study will also be shown for various types of location-scale distributions to demonstrate the efficiency of the proposed method for future observations.

URI

http://cmstatistics.org/CMStatistics2015/docs/BoA%20CFE-CMStatistics%202015.pdf?20160122015943
https://hdl.handle.net/11511/80074

Conference Name

8th International Conference of the ERCIM WG on Computational and Methodological Statistics, 12 - 14 December 2015

Collections

Department of Statistics, Conference / Seminar

Suggestions

OpenMETU
Core

Estimation and hypothesis testing in stochastic regression Sazak, Hakan Savaş; Tiku, Moti Lal; İslam, Qamarul; Department of Statistics (2003) Regression analysis is very popular among researchers in various fields but almost all the researchers use the classical methods which assume that X is nonstochastic and the error is normally distributed. However, in real life problems, X is generally stochastic and error can be nonnormal. Maximum likelihood (ML) estimation technique which is known to have optimal features, is very problematic in situations when the distribution of X (marginal part) or error (conditional part) is nonnormal. Modified maximum...
Estimation and hypothesis testing in multivariate linear regression models under non normality İslam, Muhammed Qamarul (Informa UK Limited, 2017-01-01) This paper discusses the problem of statistical inference in multivariate linear regression models when the errors involved are non normally distributed. We consider multivariate t-distribution, a fat-tailed distribution, for the errors as alternative to normal distribution. Such non normality is commonly observed in working with many data sets, e.g., financial data that are usually having excess kurtosis. This distribution has a number of applications in many other areas of research as well. We use modifie...
Inference in multivariate linear regression models with elliptically distributed errors İslam, Muhammed Qamarul; Yazici, Mehmet (2014-08-01) In this study we investigate the problem of estimation and testing of hypotheses in multivariate linear regression models when the errors involved are assumed to be non-normally distributed. We consider the class of heavy-tailed distributions for this purpose. Although our method is applicable for any distribution in this class, we take the multivariate t-distribution for illustration. This distribution has applications in many fields of applied research such as Economics, Business, and Finance. For estimat...
Cluster based model diagnostic for logistic regression Tanju, Özge; Kalaylıoğlu Akyıldız, Zeynep Işıl; Department of Statistics (2016) Model selection methods are commonly used to identify the best approximation that explains the data. Existing methods are generally based on the information theory, such as Akaike Information Criterion (AIC), corrected Akaike Information Criterion (AICc), Consistent Akaike Information Criterion (CAIC), and Bayesian Information Criterion (BIC). These criteria do not depend on any modeling purposes. In this thesis, we propose a new method for logistic regression model selection where the modeling purpose is c...
Parameter estimation in generalized partial linear models with Tikhanov regularization Kayhan, Belgin; Karasözen, Bülent; Department of Scientific Computing (2010) Regression analysis refers to techniques for modeling and analyzing several variables in statistical learning. There are various types of regression models. In our study, we analyzed Generalized Partial Linear Models (GPLMs), which decomposes input variables into two sets, and additively combines classical linear models with nonlinear model part. By separating linear models from nonlinear ones, an inverse problem method Tikhonov regularization was applied for the nonlinear submodels separately, within the e...

Citation Formats

B. Sürücü, “Hazard rate and reliability function predictions for future observations,” presented at the 8th International Conference of the ERCIM WG on Computational and Methodological Statistics, 12 - 14 December 2015, 2015, Accessed: 00, 2021. [Online]. Available: http://cmstatistics.org/CMStatistics2015/docs/BoA%20CFE-CMStatistics%202015.pdf?20160122015943.