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Noor Akma Ibrahim
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Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Engineering Environmental Science Industrial & Manufacturing Engineering Mathematics Transportation

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PEMODELAN COMPETETING RISK DENGAN DISTRIBUSI EKSPONENSIAL YANG SALING BEBAS Abdul Kudus; Noor Akma Ibrahim; Isa Daud; Mohd. Rizam Abu Bakar
STATISTIKA: Forum Teori dan Aplikasi Statistika Vol 3, No 1 (2003)
Publisher : Program Studi Statistika Unisba

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29313/jstat.v3i1.512

Abstract

Dalam makalah ini dibahas pemodelan competing risk untuk dua penyebab kegagalan. Dua variabel competing risktersebut diasumsiskan berdistribusi Eksponensial saling bebas satu sama lain. Selain pemodelan dengan distribusi eksponensialdisini juga dilakukan pemodelan dengan distribusi eksponensial campuran. Pada bagian akhir ditunjukan metode penaksirkemungkinan maksimum untuk mendapatkan taksiran parameternya.
Weighted Two-sample Test for Comparing Subdistribution Function of a Competing Risk Abdul Kudus; Noor Akma Ibrahim
STATISTIKA: Forum Teori dan Aplikasi Statistika Vol 5, No 1 (2005)
Publisher : Program Studi Statistika Unisba

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29313/jstat.v5i1.912

Abstract

The problem of testing for differences between two groups with respect to multiple competingrisk time-to-event endpoints is considered A class of two-sample test is proposed for comparing thesubdistribution of a particular type of failures. The test is based on comparing the weighted averageof subdistribution functions without making any assumption on the nature of dependence among therisks. The weight function has been chosen so that the test is distribution-free in the sense thatasymptotically valid test can be performed without assumption regarding the underlying survival andcensoring distribution. Both theoretical result and simulation evidence show that the proposedmethod attains the nominal level. We also apply the test to real data.
ON A GENERALIZATION OF THE GUMBEL DISTRIBUTION Ahmed Hurairah; Noor Akma Ibrahim; Isa bin Daud; Kassim Haron
STATISTIKA: Forum Teori dan Aplikasi Statistika Vol 3, No 1 (2003)
Publisher : Program Studi Statistika Unisba

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29313/jstat.v3i1.514

Abstract

In the paper, we proposed a generakization of the gumbel distribution. Simpel properties of the distribution arestudied.
Interval Konfidensi Serempak Bagi Rataan Respons dengan Metode Bootstrap Persentil pada Regresi Linier Sederhana Akhmad Fauzy; Noor Akma Ibrahim; Isa Daud; Mohd. Rizam Abu Bakar
STATISTIKA: Forum Teori dan Aplikasi Statistika Vol 2, No 2 (2002)
Publisher : Program Studi Statistika Unisba

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29313/jstat.v2i2.497

Abstract

Seringkali kita ingin membuat interval konfidensi bagi rataan respons pada beberapa nilai X daridata sampel yang sama. Cara yang sering digunakan dalam mengestimasi E{Yh} pada berbagai nilaiXh adalah menghitung nilai tersebut sendiri-sendiri dan ternyata mempunyai kelemahan. Hal inidisebabkan keduanya berasal dari data sampel yang sama. Bonferroni telah mencoba membuatinterval konfidensi serempak bagi nilai tersebut. Metode ini ternyata juga mempunyai kelemahan,karena data yang diperoleh membutuhkan asumsi kenormalan. Tulisan ini akan menggunakanmetode lain, yaitu metode Bootstrap persentil. Akan ditunjukkan bahwa metode bootstrap persentilmenghasilkan interval konfidensi serempak yang lebih baik apabila dibandingkan dengan metodeBonferroni.
INTERVAL ESTIMATION FOR TWO PARAMETERS EXPONENTIAL DISTRIBUTION UNDER MULTIPLE TYPE-II CENCORING ON SIMPLE CASE WITH BOOTSTRAP PERCENTILE Akhmad Fauzy; Noor Akma Ibrahim; Isa Daud; Mohd. Rizam Abu Bakar
STATISTIKA: Forum Teori dan Aplikasi Statistika Vol 3, No 1 (2003)
Publisher : Program Studi Statistika Unisba

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29313/jstat.v3i1.515

Abstract

In the article, two methods are proposed to give the interval estimation for twoparameters exponential distribution under multiple type-II cencoring on simple case. Balakrishnan(1990) use approximate maximum likehood estimator to construct interval estimation. All of thesemethod need an assumtion that sample is exounentially distributed. Method give shorter intervalthan the traditional method and this method does not need an assumtion that the sample isdistributed exponentially
Monitoring Variability in Complex Manufacturing Process: Data Analysis Viewpoint with Application Djauhari, Maman Abdurachman; Mohd Asrah, Norhaidah; Irianto, Irianto; Ibrahim, Noor Akma
International Journal of Supply Chain Management Vol 8, No 2 (2019): International Journal of Supply Chain Management (IJSCM)
Publisher : ExcelingTech

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59160/ijscm.v8i2.2986

Abstract

To relate the control limits of Shewhart-type chart to the p-value, the control charting techniques were constructed based on statistical inference scheme. However, in daily practice of complex process variability (CPV) monitoring operation, these limits have nothing to do with the p-value. We cannot put any number to p. Instead, p is just read as “most probably”. These words mean that in practice we are finally working under data analysis scheme instead. For this reason, in this paper we introduce the application of STATIS in CPV monitoring operation. It is a data analysis method to label the sample(s) where anomalous covariance structure occurs. This method is algebraic in nature and dominated by principal component analysis (PCA) principles. The relative position of a covariance matrix among others is visually presented along the first two eigenvalues of the so-called “scalar product matrix among covariance matrices”. Its strength will be illustrated by using a real industrial example and the results, compared with those given by the current methods, are very promising. Additionally, root causes analysis is also provided. However, since STATIS is a PCA-like, it does not provide any control chart, i.e., the history of process performance. It is to label the anomalous sample(s). To the knowledge of the authors, the application of STATIS in complex statistical process control is an unprecedented. Thus, it will enrich the literature of this field.
Co-Authors Abdul Kudus Ahmed Hurairah Djauhari, Maman Abdurachman Irianto Irianto Isa Bin Daud Isa Daud Kassim Haron Luh Putu Ratna Sundari Mohd Asrah, Norhaidah Mohd. Rizam Abu Bakar