Maximum Smoothed Likelihood Estimation of the Centre of a Symmetric Distribution

Abstract. Incremental fit indices (IFIs) are regularly used when assessing the fit of structural equation models. IFIs are based on the comparison of the fit of a target model with that of a null model. For maximum-likelihood estimation, IFIs are usually computed by using the χ2 statistics of the maximum-likelihood fitting function (ML-χ2). However, LISREL recently changed the computation of IFIs. Since version 8.52, IFIs reported by LISREL are based on the χ2 statistics of the reweighted least squares fitting function (RLS-χ2). Although both functions lead to the same maximum-likelihood parameter estimates, the two χ2 statistics reach different values. Because these differences are especially large for null models, IFIs are affected in particular. Consequently, RLS-χ2 based IFIs in combination with conventional cut-off values explored for ML-χ2 based IFIs may lead to a wrong acceptance of models. We demonstrate this point by a confirmatory factor analysis in a sample of 2449 subjects.

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The representation of reward magnitude in the human ventral striatum and the medial orbitofrontal and ventromedial prefrontal cortex – An activation likelihood estimation meta-analysis with implications for a fundamental understanding of the human brain and substance abuse disorders

Pharmacopsychiatry ◽

10.1055/s-0031-1292500 ◽

2011 ◽

Vol 44 (06) ◽

Author(s):

L Kaps ◽

O Gruber ◽

F Falkai ◽

EK Diekhof

Keyword(s):

Substance Abuse ◽

Prefrontal Cortex ◽

Human Brain ◽

Ventral Striatum ◽

Meta Analysis ◽

Likelihood Estimation ◽

Ventromedial Prefrontal Cortex ◽

Activation Likelihood Estimation ◽

Fundamental Understanding ◽

Substance Abuse Disorders

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Approximate Maximum Likelihood Estimation Using a 3D GNSS Simulator for Positioning in MP/NLOS Conditions

Proceedings of the 30th International Technical Meeting of The Satellite Division of the Institute of Navigation (ION GNSS+ 2017) ◽

10.33012/2017.15213 ◽

2017 ◽

Cited By ~ 2

Author(s):

Nabil Kbayer ◽

Mohamed Sahmoudi ◽

H�ctor Ortega ◽

C�dric Rouch

Keyword(s):

Maximum Likelihood ◽

Maximum Likelihood Estimation ◽

Likelihood Estimation ◽

Approximate Maximum Likelihood

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Cerebral glucose metabolism and Cerebral blood flow in Hypothyroidism and Hyperthyroidism: An activation likelihood estimation Meta-analysis

Endocrine Abstracts ◽

10.1530/endoabs.63.p172 ◽

2019 ◽

Author(s):

Kyoungjune Pak ◽

Bo hyun Kim ◽

Mijin Kim ◽

Keunyoung Kim

Keyword(s):

Blood Flow ◽

Cerebral Blood Flow ◽

Glucose Metabolism ◽

Meta Analysis ◽

Likelihood Estimation ◽

Cerebral Glucose Metabolism ◽

Activation Likelihood Estimation

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Missing Data - Better "Not to Have Them", but What If You Do? (Part 1)

Marketing ZFP ◽

10.15358/0344-1369-2019-4-21 ◽

2019 ◽

Vol 41 (4) ◽

pp. 21-32

Author(s):

Dirk Temme ◽

Sarah Jensen

Keyword(s):

Missing Data ◽

Statistical Power ◽

Missing Values ◽

Graphical Representation ◽

Marketing Research ◽

Likelihood Estimation ◽

Parameter Estimates ◽

Full Information Maximum Likelihood ◽

Definition Of ◽

Traditional Approaches

Missing values are ubiquitous in empirical marketing research. If missing data are not dealt with properly, this can lead to a loss of statistical power and distorted parameter estimates. While traditional approaches for handling missing data (e.g., listwise deletion) are still widely used, researchers can nowadays choose among various advanced techniques such as multiple imputation analysis or full-information maximum likelihood estimation. Due to the available software, using these modern missing data methods does not pose a major obstacle. Still, their application requires a sound understanding of the prerequisites and limitations of these methods as well as a deeper understanding of the processes that have led to missing values in an empirical study. This article is Part 1 and first introduces Rubin’s classical definition of missing data mechanisms and an alternative, variable-based taxonomy, which provides a graphical representation. Secondly, a selection of visualization tools available in different R packages for the description and exploration of missing data structures is presented.

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

InPrime: Indonesian Journal of Pure and Applied Mathematics ◽

10.15408/inprime.v2i1.14556 ◽

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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