scholarly journals PERBANDINGAN PRINCIPAL AXIS FACTORING DAN MAXIMUM LIKELIHOOD DALAM MENENTUKAN FAKTOR DOMINAN YANG MEMPENGARUHI PEMBELAJARAN NAHWU SHOROF (STUDI KASUS PONDOK PESANTREN ROUDLOTUL MUTA’ALLIMIN PUTRI)

2021 ◽  
Vol 15 (4) ◽  
pp. 785-796
Author(s):  
Tanwirotul Khusna ◽  
Rachmadania Akbarita ◽  
Risang Narendra
Keyword(s):  
Maximum Likelihood ◽  
Principal Axis ◽  
Mean Squared Error ◽  
Boarding School ◽  
Extraction Methods ◽  
Likelihood Method ◽  
Dominant Factor ◽  
Teaching Staff ◽  
Maximum Likelihood Methods ◽  
Principal Axis Factoring

This study discusses the dominant factors that influence the success of learning nahwu shorof at the Roudlotul Mutaalimin Islamic Boarding School for the daughter of Minggirsari Village. Determining the dominant factor is done to maximize the quality of education in the boarding school, so that the interest of prospective students is increasing. In this study, two extraction methods were compared, namely the Principal Axis Factoring and Maximum Likelihood methods. There are 13 variables that affect the success of nahwu shorof learning, namely the natural environment (P1), social environment (P23), curriculum (P49), madrasa program (P1012), facilities and facilities (P1315), teaching staff (P1619), condition of physiological (P2021), condition of the five senses (P22), interest in learning (P2325), intelligence of students (P26), student talent (P27), motivation of students (P28), cognitive ability (P2930). The purpose of this study, namely to determine the most appropriate extraction method used in the analysis. The result of this study is the Maximum Likelihood method which is more appropriate than the Principal Axis Factoring method, because it has a smaller RMSE (Root Mean Squared Error) value.

scholarly journals Estimasi Parameter Model Autoregressive dengan Metode Yule Walker, Least Square, dan Maximum Likelihood (Studi Kasus Data ROA BPRS di Indonesia)

2021 ◽  
Vol 1 (1) ◽  
pp. 1-6
Author(s):  
Maulida Nurhidayati
Keyword(s):  
Maximum Likelihood ◽  
Maximum Likelihood Method ◽  
Least Square ◽  
Likelihood Method ◽  
Model Parameters ◽  
Likelihood Methods ◽  
Simulation Data ◽  
Sample Data ◽  
Maximum Likelihood Methods ◽  
Modeling Data

The Autoregressive model is a time series univariate model for stationary models. In estimating parameters on this model can be done by several methods, namely yule-walker method, Least Square, and Maximum Likelihood. Each method has a different principle for estimating model parameters so that the results obtained will also be different. Based on this, in this study, the AR(1) model parameter estimation was estimated by generating data simulated 1000 times to see the performance of Yule-Walker, Least Square, and Maximum Likelihood methods. In addition, the comparison of these three methods is also done on ROA BPRS data that follows the AR(1) model. The results showed that the Maximum Likelihood method was able to provide mode results and comparison of the most suitable estimation results for simulation data and produce the smallest MAE values in the data in sample and MAPE, MSE, and MAE the smallest in the out sample data. These results show that the Maximum Likelihood method is the best method for modeling data that follows the AR(1) model.

scholarly journals A Novel Generalized Family of Distributions for Engineering and Life Sciences Data Applications

2021 ◽  
Vol 2021 ◽  
pp. 1-16
Author(s):  
Najma Salahuddin ◽  
Alamgir Khalil ◽  
Wali Khan Mashwani ◽  
Habib Shah ◽  
Pijitra Jomsri ◽  
...  
Keyword(s):  
Maximum Likelihood ◽  
Mean Squared Error ◽  
Residual Life ◽  
Parametric Estimation ◽  
Maximum Likelihood Estimates ◽  
Likelihood Method ◽  
Mean Residual Life ◽  
Estimation Of Parameters ◽  
Lifetime Distributions ◽  
The Family

In this paper, a new method is proposed to expand the family of lifetime distributions. The suggested method is named as Khalil new generalized family (KNGF) of distributions. A special submodel, termed as Khalil new generalized Pareto (KNGP) distribution, is investigated from the family with one shape and two scale parameters. A number of mathematical properties of the submodel have been derived including moments, moment-generating function, quantile function, entropy measures, order statistics, mean residual life function, and maximum likelihood method for the estimation of parameters. The proposed distribution is very flexible in its nature covering several hazard rate shapes (symmetric and asymmetric). To examine the performance of the maximum likelihood estimates in terms of their bias and mean squared error using simulated samples, a simulation study is carried out. Furthermore, parametric estimation of the model is conferred using the method of maximum likelihood, and the practicality of the proposed family is illustrated with the help of real datasets. Finally, we hope that the new suggested flexible KNGF may produce useful models for fitting monotonic and nonmonotonic data related to survival analysis and reliability analysis.

scholarly journals A new family of polyno-expo-trigonometric distributions with applications

2019 ◽  
Vol 22 (04) ◽  
pp. 1950027
Author(s):  
Farrukh Jamal ◽  
Christophe Chesneau
Keyword(s):  
Maximum Likelihood ◽  
Maximum Likelihood Method ◽  
Mean Squared Error ◽  
Real Life ◽  
Likelihood Method ◽  
Lifetime Data ◽  
New Family ◽  
Squared Error ◽  
The Mean ◽  
Special Case

In this paper, a new family of polyno-expo-trigonometric distributions is presented and investigated. A special case using the Weibull distribution, with three parameters, is considered as statistical model for lifetime data. The estimation of the parameters is performed with the maximum likelihood method. A numerical simulation study verifies that the bias and the mean squared error of the maximum likelihood estimators tend to zero as the sample size is increased. Three real life datasets are then analyzed. We show that our model has a good fit in comparison to the other well-known powerful models in the literature.

scholarly journals Properties of Maximum Likelihood Male Fertility Estimation in Plant Populations

Genetics ◽  
1998 ◽  
Vol 149 (2) ◽  
pp. 1099-1103
Author(s):  
M T Morgan
Keyword(s):  
Maximum Likelihood ◽  
Male Fertility ◽  
Maximum Likelihood Method ◽  
Population Level ◽  
Likelihood Method ◽  
Phenotypic Trait ◽  
Likelihood Methods ◽  
Plant Populations ◽  
Maximum Likelihood Methods ◽  
Fertility Estimation

Abstract Computer simulations are used to evaluate maximum likelihood methods for inferring male fertility in plant populations. The maximum likelihood method can provide substantial power to characterize male fertilities at the population level. Results emphasize, however, the importance of adequate experimental design and evaluation of fertility estimates, as well as limitations to inference (e.g., about the variance in male fertility or the correlation between fertility and phenotypic trait value) that can be reasonably drawn.

scholarly journals Estimation of Rates of Recombination and Migration in Populations of Plant Pathogens—A Reply

2000 ◽  
Vol 90 (4) ◽  
pp. 324-326 ◽  
Author(s):  
J. Zhan ◽  
C. C. Mundt ◽  
B. A. McDonald
Keyword(s):  
Maximum Likelihood ◽  
Plant Pathogens ◽  
Original Method ◽  
Likelihood Method ◽  
Data Sets ◽  
Likelihood Methods ◽  
New Methods ◽  
Maximum Likelihood Methods ◽  
Fungal Populations ◽  
And Migration

We find that the maximum likelihood method proposed by J. K. M. Brown has deficiencies that limit its usefulness for actual data sets. We propose two alternative statistical methods based on maximum likelihood that could be used to quantify rates of recombination and immigration in fungal populations. We also show that minor modification of our original method, which was based upon posterior probabilities, leads to a result that is identical to one of the maximum likelihood methods. Our previous estimates of the relative contributions of sexual reproduction, asexual reproduction, and immigration to the genetic structure of a Mycosphaerella graminicola population did not change significantly following reanalysis of our data with these new methods.

Fast Maximum Likelihood Localization of Near-Field Sources

2013 ◽  
Vol 816-817 ◽  
pp. 493-496 ◽  
Author(s):  
Lin Xue ◽  
Hong Cun Zhai
Keyword(s):  
Computational Complexity ◽  
Maximum Likelihood ◽  
Gaussian Noise ◽  
Maximum Likelihood Method ◽  
Mean Squared Error ◽  
Near Field ◽  
Distance Estimation ◽  
Likelihood Method ◽  
Signal Model ◽  
Sound Sources

Conventional methods for locating near-field sources generally suffer performance degradation when the assumption of uniform spatial Gaussian noise does not hold. In this paper study the scenario of non-uniform spatial Gaussian noise. First we construct the near-field signal model based on planar sensor array and derive the maximum likelihood method for obtaining the azimuth and distance of sound sources, then we proposed two fast algorithms-stepwise-concentrated maximum likelihood method(SML) and approximate maximum likelihood method(AML) to reduce the high computational complexity of maximum likelihood localization method. Simulation results show that the two proposed methods outperform conventional maximum likelihood method, with lower computational complexity and less mean squared error of both azimuth estimation and distance estimation.

scholarly journals Classical Estimation of the Index Spmk and Its Confidence Intervals for Power Lindley Distributed Quality Characteristics

2020 ◽  
Vol 2020 ◽  
pp. 1-17
Author(s):  
Ghadah Alomani ◽  
Refah Alotaibi ◽  
Sanku Dey ◽  
Mahendra Saha
Keyword(s):  
Maximum Likelihood ◽  
Confidence Intervals ◽  
Maximum Likelihood Method ◽  
Mean Squared Error ◽  
Quality Characteristics ◽  
Likelihood Method ◽  
Squared Error ◽  
Relative Coverage ◽  
Anderson Darling ◽  
Percentile Bootstrap

The process capability index (PCI) has been introduced as a tool to aid in the assessment of process performance. Usually, conventional PCIs perform well under normally distributed quality characteristics. However, when these PCIs are employed to evaluate nonnormally distributed process, they often provide inaccurate results. In this article, in order to estimate the PCI Spmk when the process follows power Lindley distribution, first, seven classical methods of estimation, namely, maximum likelihood method of estimation, ordinary and weighted least squares methods of estimation, Cramèr–von Mises method of estimation, maximum product of spacings method of estimation, Anderson–Darling, and right-tail Anderson–Darling methods of estimation, are considered and the performance of these estimation methods based on their mean squared error is compared. Next, three bootstrap confidence intervals (BCIs) of the PCI Spmk, namely, standard bootstrap, percentile bootstrap, and bias-corrected percentile bootstrap, are considered and compared in terms of their average width, coverage probability, and relative coverage. Besides, a new cost-effective PCI, namely, Spmkc is introduced by incorporating tolerance cost function in the index Spmk. To evaluate the performance of the methods of estimation and BCIs, a simulation study is carried out. Simulation results showed that the maximum likelihood method of estimation performs better than their counterparts in terms of mean squared error, while bias-corrected percentile bootstrap provides smaller confidence length (width) and higher relative coverage than standard bootstrap and percentile bootstrap across sample sizes. Finally, two real data examples are provided to investigate the performance of the proposed procedures.

Estimating the Cost of Car Warranty in Indonesia using the Gertsbakh-Kordonsky Method

2020 ◽  
Vol 2 (1) ◽  
pp. 33-40
Author(s):  
Anggis Sagitarisman ◽  
Aceng Komarudin Mutaqin
Keyword(s):  
Maximum Likelihood ◽  
Likelihood Estimation ◽  
Type A ◽  
Claim Data ◽  
Likelihood Method ◽  
Selling Price ◽  
One Dimensional ◽  
Warranty Period ◽  
Kolmogorov Smirnov ◽  
Warranty Costs

AbstractCar manufacturers in Indonesia need to determine reasonable warranty costs that do not burden companies or consumers. Several statistical approaches have been developed to analyze warranty costs. One of them is the Gertsbakh-Kordonsky method which reduces the two-dimensional warranty problem to one dimensional. In this research, we apply the Gertsbakh-Kordonsky method to estimate the warranty cost for car type A in XYZ company. The one-dimensional data will be tested using the Kolmogorov-Smirnov to determine its distribution and the parameter of distribution will be estimated using the maximum likelihood method. There are three approaches to estimate the parameter of the distribution. The difference between these three approaches is in the calculation of mileage for units that do not claim within the warranty period. In the application, we use claim data for the car type A. The data exploration indicates the failure of car type A is mostly due to the age of the vehicle. The Kolmogorov-Smirnov shows that the most appropriate distribution for the claim data is the three-parameter Weibull. Meanwhile, the estimated using the Gertsbakh-Kordonsky method shows that the warranty costs for car type A are around 3.54% from the selling price of this car unit without warranty i.e. around Rp. 4,248,000 per unit.Keywords: warranty costs; the Gertsbakh-Kordonsky method; maximum likelihood estimation; Kolmogorov-Smirnov test.                                   AbstrakPerusahaan produsen mobil di Indonesia perlu menentukan biaya garansi yang bersifat wajar tidak memberatkan perusahaan maupun konsumen. Beberapa pendekatan statistik telah dikembangkan untuk menganalisis biaya garansi. Salah satunya adalah metode Gertsbakh-Kordonsky yang mereduksi masalah garansi dua dimensi menjadi satu dimensi. Pada penelitian ini, metode Gertsbakh-Kordonsky akan digunakan untuk mengestimasi biaya garansi untuk mobil tipe A pada perusahaan XYZ. Data satu dimensi hasil reduksi diuji kecocokan distribusinya menggunakan uji kecocokan Kolmogorov-Smirnov dan taksiran parameter distribusinya menggunakan metode penaksir kemungkinan maksimum. Ada tiga pendekatan yang digunakan untuk menaksir parameter distribusi. Perbedaan dari ketiga pendekatan tersebut terletak pada perhitungan jarak tempuh untuk unit yang tidak melakukan klaim dalam periode garansi. Sebagai bahan aplikasi, kami menggunakan data klaim unit mobil tipe A. Hasil eksplorasi data menunjukkan bahwa kegagalan mobil tipe A lebih banyak disebabkan karena faktor usia kendaraan. Hasil uji kecocokan distribusi untuk data hasil reduksi menunjukkan bahwa distribusi yang cocok adalah distribusi Weibull 3-parameter. Sementara itu, hasil perhitungan taksiran biaya garansi menunjukan bahwa taksiran biaya garansi untuk unit mobil tipe A sekitar 3,54% dari harga jual unit mobil tipe A tanpa garansi, atau sekitar Rp. 4.248.000,- per unit.Kata Kunci: biaya garansi; metode Gertsbakh-Kordonsky; penaksiran kemungkinan maksimum; uji Kolmogorov-Smirnov.

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