On parameter estimation of a replicated linear functional relationship model for circular variables

Mokhtar, Nurkhairany Amyra; Zubairi, Yong Zulina; Hussin, Abdul Ghapor; Yunus, Rossita M.

doi:10.11113/matematika.v33.n2.1010

Cited by 3 publications

(4 citation statements)

References 5 publications

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“…Based on the simulation performances, it is possible to conclude that the suggested model is adequate for modelling circular data with very little bias in general. In contrast to the earlier study of replicated functional model by Mokhtar et al (2017), this proposed replicated linear functional relationship model is able to estimate the parameters without having to assume the ratio of the error concentration parameter and it considers all parameters involved…”

Section: Simulation Resultsmentioning

confidence: 98%

“…The unidentifiability problem in an unreplicated linear functional relationship model (LFRM) can be avoided if the ratio of error concentration parameter  is known in order to estimate the parameters (Mokhtar et al, 2017). However, the value  is unknown in most actual circumstances because the information is either unavailable or not provided by field researchers.…”

Section: Introductionmentioning

confidence: 99%

“…ii XY and 1, 2, , ip = . In this paper, we extended the works of Mokhtar et al (2017), to derive all the parameter estimates of the model.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Parameter Estimation for Balance-Equal Replicated Linear Functional Relationship Model: An Application to the Wind Directional Data

SAARI¹,

HUSSIN²,

ZUBAIRI³

et al. 2023

JOSMA

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A linear functional relationship model can be used to model the relationship between two circular variables when both variables are observed with errors. When the ratio of error concentration parameter is unknown, it is suggested that the replicated linear functional relationship model be used. The purpose of this paper is to present all the parameter estimates of the replicated linear functional relationship model for balance replicates and equal circular variables. Simulations studies are performed to study understand the behavior of the parameter estimators for the balance-equal replicated linear functional relationship model. The empirical results obtained suggest that the proposed parameter estimation method performs well with small bias. A simple application of the model is demonstrated by analyzing a real dataset of a wind directional data.

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Section: Simulation Resultsmentioning

confidence: 98%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Parameter Estimation for Balance-Equal Replicated Linear Functional Relationship Model: An Application to the Wind Directional Data

SAARI¹,

HUSSIN²,

ZUBAIRI³

et al. 2023

JOSMA

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“…The identification of an effective probability distribution to characterize the FFA at a given location is often crucial. The methods that are typically used for parameter estimation of the distribution are the maximum likelihood estimation and the L-moment method [9,10,11]. Many studies have explored and applied the L-moment approach to FFA since 1990, including hydrological studies [12].…”

Section: Introductionmentioning

confidence: 99%

Flood Frequency Analysis using L-Moment For Segamat River

Badyalina,

Mokhtar,

Mat Jan

et al. 2021

MATEMATIKA

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Flood frequency analysis is critical in water system design and estimating flood recurrence. This study aims to conduct the flood frequency analysis on Segamat River streamflow site to find the optimum distribution that fits the flood frequency data. In terms of estimating parameters, the L-moment method is more robust and more efficient than the maximum likelihood method. Besides, the L-moment method is not affected by sampling variability. Therefore, in this study, we applied the technique of L-moment for parameter estimation on five candidate distributions, the generalised Pareto (GPA) distribution, generalised extreme value (GEV) distribution, generalised logistic (GLO) distribution, log-Pearson 3 (LP3) distribution, and log-normal (LN3) distribution. The rank score approach is implemented to determine the optimum distribution for the annual Segamat River peak flow. Probability distribution identification is essential and it is a fundamental step in statistical analysis. The goodness of fit test and efficiency assessment are employed to evaluate the distributions' performance. The results show that the LN3 distribution is selected as the optimum function for the yearly peak flow for the Segamat River streamflow site. The outcome of this study can be used to understand the flood frequency analysis for the Segamat River.

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COVRATIO statistic for replicated linear functional relationship model

Arif

Zubairi

Hussin

2021

J. Phys.: Conf. Ser.

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Replicated linear functional relationship model (LFRM) can be categorized under the errors-in-variables model where variables involved are measured with error. However, the presence of outliers in dataset significantly impacts the parameter estimation. We extend the use of the COVRATIO statistic which has been successfully used in unreplicated LFRM for detecting the outliers. A simulation study is used to obtain the cut-off point at 10% upper percentiles. An illustration of this procedure is presented for its potential in a real data set. The procedure successfully identifies the outlier present in the data set.

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On parameter estimation of a replicated linear functional relationship model for circular variables

Cited by 3 publications

References 5 publications

Parameter Estimation for Balance-Equal Replicated Linear Functional Relationship Model: An Application to the Wind Directional Data

Parameter Estimation for Balance-Equal Replicated Linear Functional Relationship Model: An Application to the Wind Directional Data

Flood Frequency Analysis using L-Moment For Segamat River

COVRATIO statistic for replicated linear functional relationship model

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