Maximum Likelihood Estimation—A Reliable Statistical Method for Hydrate Nucleation Data Analysis

Abstract Determination of normal ranges from laboratory data containing undectable values is a frequently encountered problem in the radioimmunoassay of peptide hormones. In the past, such determinations usually have been based on the mid-point method or the one-end Winsorized method. A graphic method involving the use of probability paper has also been reported. We propose that the maximum-likelihood estimation is a more appropriate statistical method for the determination of normal range from this type of data (Type I censored data). With this method, the mean and standard deviation, and hence the tolerance limits, can be estimated. We used the maximum-likelihood estimation method to determine the normal range of serum thyrotropin values obtained from 93 healthy subjects, based on a log normal distribution. Although the serum thyrotropin content was undetectable in 14% of the subjects, a normal range could be calculated. Using tolerance limits for 95% coverage of the population with 90% confidence, we calculated the normal range of thyrotropin to be 0.51-5.75 milliunits/L, with a mean value of 1.71 milliunits/L, and predicted that 91.4% of undetectable serum thyrotropin values will fall within the normal range.

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Maximum likelihood estimation for second level fMRI data analysis with expectation trust region algorithm

Magnetic Resonance Imaging ◽

10.1016/j.mri.2013.10.007 ◽

2014 ◽

Vol 32 (2) ◽

pp. 132-149 ◽

Cited By ~ 1

Author(s):

Xingfeng Li ◽

Damien Coyle ◽

Liam Maguire ◽

Thomas Martin McGinnity

Keyword(s):

Data Analysis ◽

Maximum Likelihood ◽

Maximum Likelihood Estimation ◽

Trust Region ◽

Likelihood Estimation ◽

Fmri Data ◽

Trust Region Algorithm ◽

Fmri Data Analysis

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Effective estimation algorithm for parameters of multivariate Farlie–Gumbel–Morgenstern copula

Japanese Journal of Statistics and Data Science ◽

10.1007/s42081-021-00118-y ◽

2021 ◽

Author(s):

Shuhei Ota ◽

Mitsuhiro Kimura

Keyword(s):

Parameter Estimation ◽

Data Analysis ◽

Computational Complexity ◽

Maximum Likelihood ◽

Maximum Likelihood Estimation ◽

Asymptotic Normality ◽

Likelihood Estimation ◽

Real Data ◽

Estimation Algorithm ◽

Simulation Studies

AbstractThis paper focuses on the parameter estimation for the d-variate Farlie–Gumbel–Morgenstern (FGM) copula ($$d\ge 2$$ d ≥ 2 ), which has $$2^d-d-1$$ 2 d - d - 1 dependence parameters to be estimated; therefore, maximum likelihood estimation is not practical for a large d from the viewpoint of computational complexity. Besides, the restriction for the FGM copula’s parameters becomes increasingly complex as d becomes large, which makes parameter estimation difficult. We propose an effective estimation algorithm for the d-variate FGM copula by using the method of inference functions for margins under the restriction of the parameters. We then discuss its asymptotic normality as well as its performance determined through simulation studies. The proposed method is also applied to real data analysis of bearing reliability.

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Analisis Kepuasan Pelanggan PT. Sucofindo Melalui Kualitas Layanan, dan Citra Perusahaan

JMM17: Jurnal Ilmu Ekonomi dan Manajemen ◽

10.30996/jmm17.v1i02.385 ◽

2015 ◽

Vol 1 (02) ◽

Author(s):

Feliks Anggia Binsar K Panjaitan

Keyword(s):

Data Analysis ◽

Maximum Likelihood ◽

Customer Satisfaction ◽

Maximum Likelihood Estimation ◽

Service Quality ◽

Likelihood Estimation ◽

Expected Value ◽

Sample Number ◽

Data Analysis Result ◽

What Matters

Abstract: Related business activities performed by PT. Sucofindo, which should consider the satisfaction for customers, thus this research, wish to find out what are the company should do, and also what matters are the customers expect, so that PT. Sucofindo can increase its performance as good as possible.The population was all customers of PT. Sucofindo. The estimation used was Maximum Likelihood Estimation (ML), and sample number would be researched was as large as 100. Data analysis result, indicated values from GFI, AGFI, TLI, CFI, RMSEA and CMIN/DF of 0.931, 0.877, 0.996, 0.997, 0.023 and 1.052, respectively, which all of those in the expected value range.The result of research indicated that: 1. Service quality is influential on image. 2. Service quality is influential on customer satisfaction. 3. Company image is influential on customer satisfaction. 4. Service quality is influential on customer satisfaction, indirectly via company image.Key Words: Service quality, company image, customer satisfaction

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