A robust spatial autoregressive scalar-on-function regression with t-distribution

Huang, Tingting; Saporta, Gilbert; Wang, Huiwen; Wang, Shanshan

doi:10.1007/s11634-020-00384-w

Cited by 7 publications

(3 citation statements)

References 60 publications

(71 reference statements)

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“…Penelitian terdahulu yang telah membahas tentang model RSAR. Salah satunya adalah Huang dkk [8] mengenai RSAR mengunakan scalar-on-function regression dengan distribusi-t, Zhi Yang Tho dkk [9] mengenai pengaplikasian estimasi robust pada model spasial autoregresif untuk data hibah federal AS. Yasin dkk [10] menggunakan regresi spasial robust estimasi-M. Mastuti dkk [11] yang menerapkan model RSAR pada pemodelan terhadap data pendapatan asli daerah di pulau Jawa.…”

Section: Pendahuluanunclassified

Pemodelan Gizi Buruk Balita Di Indonesia Dengan Model Robust Spasial Autoregresif

Abrari,

Yanuar,

Devianto

2023

JSMS

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Gizi buruk merupakan suatu keadaan kekurangan konsumsi zat gizi yang disebabkan oleh rendahnya konsumsi energi protein dalam makanan sehari-hari, ditandai dengan berat dan tinggi badan di bawah rata-rata. Penelitian ini bertujuan untuk menentukan model terbaik pada kasus gizi buruk balita serta melihat faktor-faktor apa saja yang mempengaruhi gizi buruk balita pada provinsi-provinsi di Indonesia. Data yang digunakan adalah data hasil studi status gizi Indonesia oleh Kementerian Kesehatan Republik Indonesia. Untuk mendapatkan model terbaik, diperlukan model regresi spasial dengan mempertimbangkan pengaruh spasial suatu daerah. Metode regresi spasial yang menunjukkan adanya efek spasial pada variabel terikatnya disebut Spatial Autoregressive Model (SAR). Pada kasus tertentu, pengujian efek spasial yang melibatkan data pencilan menyebabkan suatu metode gagal dalam menangani efek spasial tersebut sehingga perlu adanya kombinasi model SAR dengan metode regresi robust yang membentuk Robust Spatial Autoregressive Model (RSAR). Hasil penelitian menunjukkan bahwa variabel persentase asuransi kesehatan dan akses air bersih berpengaruh terhadap gizi buruk balita. RSAR M-estimator mengakomodir keberadaan outlier dalam model regresi spasial, hal ini ditunjukkan dengan penurunan RMSE yang disebabkan oleh perubahan parameter koefisien penduga. Model Robust-SAR merupakan model terbaik karena memiliki nilai RMSE terkecil serta robust terhadap spatial outlier.

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Section: Pendahuluanunclassified

Pemodelan Gizi Buruk Balita Di Indonesia Dengan Model Robust Spasial Autoregresif

Abrari,

Yanuar,

Devianto

2023

JSMS

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“…Here, this paper introduces the T-distribution in statistics [20]. T-distribution is a special distribution function, which contains parameter degree of freedom n. The smaller n is, the flatter the curve is.…”

Section: B T-distribution Variationmentioning

confidence: 99%

A Hybrid Sparrow Search Algorithm Based on Constructing Similarity

Liu

Wang

2021

IEEE Access

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Sparrow search algorithm (SSA) is easy to fall into local convergence and convergence stagnation. In order to solve these problems, this paper introduced Circle chaos map into the original SSA to improve its global search ability at the beginning of iteration. Meanwhile, it introduced T-distribution variation to affect the sparrow population position update rules in different iteration periods. Finally, we constructed the ''similarity function'' to measure the ''dispersion'' of the sparrow population, and formulated the search rules of the sparrow population under different ''dispersion''. In order to test the specific optimization performance of the proposed algorithm, the test results of 54 test functions are compared with those of 9 other algorithms which are widely used, and then the test results are analyzed using non-parametric tests in statistics. At the same time, this paper introduces this algorithm into three concrete engineering test problems for testing. The results of these tests all prove that the proposed algorithm has stronger global optimization ability and higher convergence precision compared with other algorithms.

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“… Bayesian: The Bayesian estimation methods are present in some papers ,but we only mention these two papers in this part: the Bayesian bandwidth estimation and semi-metric selection for a functional partial linear model with unknown error density [36,37].  Spatial: The spatial variability is considered in many research articles such as The partial functional linear spatial regression autoregressive model with spatial dependence responses [38], with two-stage estimator based on quasi-maximum likelihood estimation (QMLE) method and local linear regression method [39], studying the asymptotic normality of the parametric component, and probability convergence with the rate of the nonparametric component [40], B-spline approximation for slope function and residualbased approach for pointwise confidence-intervals [41], the robust spatial autoregressive model with t-distribution error terms with an expectationmaximization algorithm [42].  Robust: Existing outliers in the data or violations from distributional assumptions yield to the robust methods such as the sieve M-estimator for semi-functional linear model [43], with polynomial splines to approximate the slope parameter and resistance to heavy-tailed errors or outliers in the response [44], different estimators such as M-estimators with bi-square function, GM-estimator with Huber function, LMS-estimator and LTS-estimators [45], estimation based on exponential squared loss and FPCA [46], estimation based on the class of scale mixtures of normal (SMN) distributions for measurement errors and Bayesian framework with MCMC algorithm [47], Robust MM-estimators with B-Spline approximation [48], with modal regression [49] and a modified Huber's function with tail function with a data-driven procedure for selecting the tuning parameters [50].…”

Section: Other Extensionsmentioning

confidence: 99%

A Literature Review of Semi-functional Partial Linear Regression Models

Fayaz¹

2021

Preprint

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Background: In the functional data analysis (FDA), the hybrid or mixed data are scalar and functional datasets. The semi-functional partial linear regression model (SFPLR) is one of the first semiparametric models for the scalar response with hybrid covariates. Various extensions of this model are explored and summarized. Methods: Two first research articles, including “semi-functional partial linear regression model”, and “Partial functional linear regression” have more than 300 citations in Google Scholar. Finally, only 106 articles remained according to the inclusion and exclusion criteria such as 1) including the published articles in the ISI journals and excluding 2) non-English and 3) preprints, slides, and conference papers. We use the PRISMA standard for systematic review. Results: The articles are categorized into the following main topics: estimation procedures, confidence regions, time series, and panel data, Bayesian, spatial, robust, testing, quantile regression, varying Coefficient Models, Variable Selection, Single-index model, Measurement error, Multiple Functions, Missing values, Rank Method and Others. There are different applications and datasets such as the Tecator dataset, air quality, electricity consumption, and Neuroimaging, among others. Conclusions: SFPLR is one of the most famous regression modeling methods for hybrid data that has a lot of extensions among other models.

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A robust spatial autoregressive scalar-on-function regression with t-distribution

Cited by 7 publications

References 60 publications

Pemodelan Gizi Buruk Balita Di Indonesia Dengan Model Robust Spasial Autoregresif

Pemodelan Gizi Buruk Balita Di Indonesia Dengan Model Robust Spasial Autoregresif

A Hybrid Sparrow Search Algorithm Based on Constructing Similarity

A Literature Review of Semi-functional Partial Linear Regression Models

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