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About 10,000 papers for “Bivariate data”

Bivariate data analysis

Carrasco Jordan, Josep Lluís · Dipòsit Digital de la Universitat de Barcelona (Universitat de Barcelona) · 2022

The document treats the main procdures to analysis data in the two sample context for porportion and mean parameters.

Cited by 0

Hotspot Detection with Bivariate Data 1 Hotspot Detection with Bivariate Data

Reza Modarres, G. P. Patil, G. P. Patil et al. · 2006

Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the agencies. [Invited Paper for the Journal of Statistical Planning and Inference, Centennial Volume, S.N. Roy]

Cited by 0

Bivariate Current Status Data

Mark J. van der Laan, Nicholas P. Jewell · Collection of Biostatistics Research Archive · 2002

In many applications, it is often of interest to estimate a bivariate distribution of two survival random variables. Complete observation of such random variables is often incomplete. If one only observes whether or not each of the individual survival times exceeds a common observed monitoring time C, then the data structure is referred to as bivariate current status data (Wang and Ding, 2000). For such data, we show that the identifiable part of the joint distribution is represented by three univariate cumulative distribution functions, namely the two marginal cumulative distribution functions, and the bivariate cumulative distribution function evaluated on the diagonal. The EM algorithm can be used to compute the full nonparametric maximum likelihood estimator of these three univariate cumulative distribution functions; however, we show that smooth functionals of these univariate cumulative cdfs can be efficiently estimated with easy to compute nonparametric maximum likelihood estimators (NPMLE), based on reduced data consisting of univariate current status observations. We use these univariate current status NPMLEs to obtain both a test of independence of the two survival random variables, and a test of goodness of fit for the copula model used in Wang & Ding (2000). Finally, we extend the data structure by allowing the presence of covariates, possibly time-dependent processes that are observed until the monitoring time C. We show that applying the locally efficient estimator, developed in van der Laan and Robins (1998), to the reduced univariate current status data yields locally efficient estimators.

Cited by 2Open access

The bivariate eye data.

Adzhar Rambli (2605708), Ibrahim Bin Mohamed (2605705), Abdul Ghapor Hussin (2605711) · Figshare · 2022

Cited by 0Open access

Modelling bivariate failure time data via bivariate extended Chen distribution

N. M. Kilany, F. G. El-Qareb · Greater South Information System · 2023

Abstract Fatal Shock, competing risks and stress models are utilized in reliability and survival analysis to characterize various application types. In this context, this paper aimed to introduce a new class of bivariate distribution based on the Marshall–Olkin type. The proposed distribution termed as the Bivariate Extended Chen (BEC) distribution so that the marginals have Extended Chen distribution. Some properties of this distribution are derived and explained. The BEC distribution parameters are estimated using the maximum likelihood method. Finally, two applications to real life applications highlight the significance and adaptability of the Bivariate Extended Chen (BEC) distribution. The BEC distribution offers a superior fit than different competing bivariate distributions, illustrating its flexibility and applicability in modelling.

Cited by 0Open access

HEURISTICS TO ANALYZE BIVARIATE DATA

Seyda Deligonul · Quality Engineering · 1998

keywords: Bivariate normal distributionCorrelationEqual probability ellipseHeuristicsLine fittingRegression

Cited by 0

Modelling bivariate failure time data via bivariate extended Chen distribution

N. M. Kilany, F. G. El-Qareb · Stochastic Environmental Research and Risk Assessment · 2023

Abstract Fatal Shock, competing risks and stress models are utilized in reliability and survival analysis to characterize various application types. In this context, this paper aimed to introduce a new class of bivariate distribution based on the Marshall–Olkin type. The proposed distribution termed as the Bivariate Extended Chen (BEC) distribution so that the marginals have Extended Chen distribution. Some properties of this distribution are derived and explained. The BEC distribution parameters are estimated using the maximum likelihood method. Finally, two applications to real life applications highlight the significance and adaptability of the Bivariate Extended Chen (BEC) distribution. The BEC distribution offers a superior fit than different competing bivariate distributions, illustrating its flexibility and applicability in modelling.

Cited by 4Open access

Modelling bivariate failure time data via bivariate extended Chen distribution

N. M. Kilany, F. G. El-Qareb · Greater South Information System · 2023

Abstract Fatal Shock, competing risks and stress models are utilized in reliability and survival analysis to characterize various application types. In this context, this paper aimed to introduce a new class of bivariate distribution based on the Marshall–Olkin type. The proposed distribution termed as the Bivariate Extended Chen (BEC) distribution so that the marginals have Extended Chen distribution. Some properties of this distribution are derived and explained. The BEC distribution parameters are estimated using the maximum likelihood method. Finally, two applications to real life applications highlight the significance and adaptability of the Bivariate Extended Chen (BEC) distribution. The BEC distribution offers a superior fit than different competing bivariate distributions, illustrating its flexibility and applicability in modelling.

Cited by 0Open access