Weighted log-rank tests for left-truncated data under Wei’s urn design: saddlepoint <i>p</i>-values and confidence intervals

Kamal, Kholoud S.; El-Raheem, Abd El-Raheem M. Abd; Abd-Elfattah, Ehab F.

doi:10.1080/10543406.2021.2010091

Cited by 8 publications

(3 citation statements)

References 28 publications

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“…Abd El-raheem and Abd-Elfattah 22 , 23 approximated the exact permutation distribution of a class of two-sample tests for cluster data under two different randomization designs. For more recent articles in the saddlepoint approximation method; see Kamal et al 24 , 25 . In the end, we can mention a number of important and basic references on the subject of saddlepoint approximations, which highlight the importance and applications of saddlepoint approximations in many branches and fields of statistics, namely: Booth and Butler 26 , Strawderman 27 , Butler 28 , Abd-Elfattah and Butler 29 , Kwok and Zheng 30 .…”

Section: Introductionmentioning

confidence: 99%

A new approach for approximating the p-value of a class of bivariate sign tests

Shanan,

Abd-Elfattah,

Abd El-Raheem

2023

Sci Rep

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Bivariate data are frequently encountered in many applied fields, including econometrics, engineering, physiology, biology, and medicine. For bivariate analysis, a wide range of non-parametric and parametric techniques can be applied. There are fewer requirements needed for non-parametric procedures than for parametric ones. In this paper, the saddlepoint approximation method is used to approximate the exact p-values of some non-parametric bivariate tests. The saddlepoint approximation is an approximation method used to approximate the mass or density function and the cumulative distribution function of a random variable based on its moment generating function. The saddlepoint approximation method is proposed in this article as an alternative to the asymptotic normal approximation. A comparison between the proposed method and the normal asymptotic approximation method is performed by conducting Monte Carlo simulation study and analyzing three numerical examples representing bivariate real data sets. In general, the results of the simulation study show the superiority of the proposed method over the asymptotic normal approximation method.

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Section: Introductionmentioning

confidence: 99%

A new approach for approximating the p-value of a class of bivariate sign tests

Shanan,

Abd-Elfattah,

Abd El-Raheem

2023

Sci Rep

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“…Daniels 33 was the first who suggested the major advantages of the SPA method and its general power and scope. Many papers were interested in approximating the exact p-value for various classes using SPA method with different randomization designs, such as, Abd-Elfattah and Butler 34 , 35 Abd-Elfattah 36 – 39 , Abd EL-Raheem and Abd-Elfattah 40 , 41 , Kamal et al 42 , 43 and Abd El-Raheem et al 44 , 45 .…”

Section: Introductionmentioning

confidence: 99%

Saddlepoint p-values for a class of location-scale tests under randomized block design

Mohamed,

Abd-Elfattah,

Abd-El-Monem

et al. 2024

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This paper deals with a class of nonparametric two-sample location-scale tests. The purpose of this paper is to approximate the exact p-value of the considered class under a randomized block design. The exact p-value of the considered class is approximated by the saddlepoint approximation method, also by the traditional method which is the normal approximation method. The saddlepoint approximation method is more accurate than the normal approximation method in approximating the exact p-value, and does not take a lot of time like the simulation method. This accuracy is proved by applying the mentioned methods to two real data sets and a simulation study.

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“…A number of statisticians have applied these diferent saddlepoint approximations to solve some statistical problems including Daniels [30]; Robinson [31]; Daniels [32]; Daniels [33]; Davison and Hinkley [34]; Daniels [35]; and Booth and Butler [36]. After that, saddlepoint applications have spread in many statistical felds such as survival analysis [37,38], regression analysis [39,40], reliability analysis [41,42], bootstrapping [43,44], resampling analysis [28,34], nonparametric statistics Abd-Elfattah and Butler [45,46]; Abd El-Raheem and Abd-Elfattah [47,48]; Kamal et al [49,50]. Here, we have mentioned few applications and references of the saddlepoint applications for rigorous proofs and more applications, see Butler [51].…”

Section: Introductionmentioning

confidence: 99%

Statistical Inference of the Class of Nonparametric Tests for the Panel Count and Current Status Data from the Perspective of the Saddlepoint Approximation

El-Raheem

Hosny

Abd-Elfattah

2023

Journal of Mathematics

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Many statisticians resort to using the asymptotic normal approximation method to carry out statistical inference for many statistical tests, especially nonparametric ones. In this article, the saddlepoint approximation method is proposed as an alternative to the asymptotic normal approximation method to carry out statistical inference for a number of nonparametric tests for an important type of data that appears frequently in many clinical studies such as cancer and tumorigenicity studies. In clinical trials, there are many strategies through which treatments are assigned to patients. Equal allocation of both treatments is a largely prevalent approach in clinical trials to eliminate experimental bias and increase power. Accordingly, the statistical analysis is carried out based on the truncated binomial design, which is one of the designs that achieve a perfect balance between the two treatments. To clarify the accuracy of the proposed approximation method, two sets of real data are analyzed, and for the same purpose, a comprehensive simulation study is carried out.

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Weighted log-rank tests for left-truncated data under Wei’s urn design: saddlepoint p-values and confidence intervals

Cited by 8 publications

References 28 publications

A new approach for approximating the p-value of a class of bivariate sign tests

A new approach for approximating the p-value of a class of bivariate sign tests

Saddlepoint p-values for a class of location-scale tests under randomized block design

Statistical Inference of the Class of Nonparametric Tests for the Panel Count and Current Status Data from the Perspective of the Saddlepoint Approximation

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