Interval Estimation in a Two Parameter Weibull Distribution Based on Type-2 Censored Data

Mweleli, Raphael Masila; Orawo, Luke Akong’o; Tamba, Cox Lwaka; Okenye, Justin Obwoge

doi:10.4236/ojs.2020.106059

Cited by 2 publications

(3 citation statements)

References 23 publications

(20 reference statements)

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“…Weibull distribution parameters were estimated using the maximum likelihood method, and their respective confidence intervals were estimated using the Wald technique. 20 Statistical power analysis was performed, and the calculated sample size was n = 8, but it was increased to n = 15 for Weibull analysis. The significance level was set at p < 0.05 within all tests.…”

Section: Discussionmentioning

confidence: 99%

“…Biaxial flexural strength data were analyzed using a two‐way ANOVA test followed by simple main effects comparisons utilizing the error term and degrees of freedom of the two‐way model with p ‐values adjusted for multiple comparisons using the Bonferroni correction. Weibull distribution parameters were estimated using the maximum likelihood method, and their respective confidence intervals were estimated using the Wald technique 20 . Statistical power analysis was performed, and the calculated sample size was n = 8, but it was increased to n = 15 for Weibull analysis.…”

Section: Methodsmentioning

confidence: 99%

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Biaxial flexural strength of different provisional restorative materials under chemo‐mechanical aging: An in vitro study

Sadek

Elbanna

2023

Journal of Prosthodontics

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PurposeDurability is a critical factor for the success of long‐term provisional restorations. This study evaluated the effect of different storage media and cyclic loading on the biaxial flexural strength of different types of provisional materials.Materials and methodsDiscs (N = 360, 10 × 2 mm) were prepared from different provisional materials; conventional (Protemp 4, Tuff‐Temp, Tempron), CAD–CAM milled (VITA CAD‐Temp, breCAM.multiCOM), and 3D‐printed (Nextdent C&B MFH) (n = 60). Each material group was subdivided into four subgroups according to the storage media (n = 15): nonaged, artificial saliva, mouthwash, and coffee. The specimens in storage media were placed in an incubator at 37°C for 4 weeks followed by 60,000 simulated chewing cycles. Biaxial flexural strength test was done. Data were analyzed using two‐way ANOVA. Weibull distribution parameters were estimated.ResultsBoth the material and aging protocol showed a significant effect on the biaxial flexural strength. Both Protemp 4 and Nextdent C&B MFH showed a significantly higher biaxial flexural strength before and after aging compared to all other materials. Protemp 4 and Tuff Temp showed a significant decrease in strength with aging. CAD–CAM materials showed higher Weibull moduli.ConclusionsThe 3D‐printed polymethylmethacrylate provisional material presents with both greater biaxial flexural strength and increased durability against chemical and mechanical aging compared to conventional and CAD–CAM milled provisional materials tested.

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Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Biaxial flexural strength of different provisional restorative materials under chemo‐mechanical aging: An in vitro study

Sadek

Elbanna

2023

Journal of Prosthodontics

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“…Due to the difficulty to estimate the location parameter of the Weibull distribution in small size of sample situations, the mostly applied is two-parameter Weibull distribution [5] [6] [7] [8] [9]. To estimate product life distribution in the situation of small size sample, H.K.T.…”

Section: Introductionmentioning

confidence: 99%

Characterizing the Relationship between Weibull Location Parameter and the Minimal Observation in a Small Size of Sample Based on Stochastic Simulation

Qin¹,

Wu²,

Xie³

2021

JAMP

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Three-parameter Weibull distribution is one of the preferable distribution models to describe product life. However, it is difficult to estimate its location parameter in the situation of a small size of sample. This paper presents a stochastic simulation method to estimate the Weibull location parameters according to a small size of sample of product life observations and a large amount of statistically simulated life date. Big data technique is applied to find the relationship between the minimal observation in a product life sample of size n (n ≥ 3) and the Weibull location parameter. An example is presented to demonstrate the applicability and the value of the big data based stochastic simulation method. Comparing with other methods, the stochastic simulation method can be applied to very small size of sample such as the sample size of three, and it is easy to apply.

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Interval Estimation in a Two Parameter Weibull Distribution Based on Type-2 Censored Data

Cited by 2 publications

References 23 publications

Biaxial flexural strength of different provisional restorative materials under chemo‐mechanical aging: An in vitro study

Biaxial flexural strength of different provisional restorative materials under chemo‐mechanical aging: An in vitro study

Characterizing the Relationship between Weibull Location Parameter and the Minimal Observation in a Small Size of Sample Based on Stochastic Simulation

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