Nonparametric Regression Models for the Analysis of Longitudinal Data

Wu, Colin O.; Tian, Xin; Yu, Kai; Wu, Mixia

doi:10.1142/9789814583312_0029

Cited by 7 publications

(12 citation statements)

References 68 publications

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“…Pada prosedur regresi nonparamterik, data akan mencari sendiri bentuk kurva regresinya tanpa dipengaruhi oleh subjektivitas peneliti. Beberapa model regresi nonparametrik yang telah dikembangkan antara lain Penalized Spline (Kadiri et al, 2010), Smoothing Spline (Eubank et al, 2004), Regresi Spline Multirespon (Lestari et al, 2010), Regresi Menggunakan Kernel (Hu et al, 2004), Kernel of Smoothing Spline (Lin et al, 2004) dan Polinomial Lokal (Wu dan Zhang, 2006) Polinomial lokal mempunyai beberapa kelebihan antara lain dapat mengurangi asimtotik bias dan menghasilkan estimasi yang baik (Welsh dan Yee, 2005). Estimasi Polinomial Lokal dapat menggunakan WLS (Weighted Least Square) dengan cara meminimumkannya (Takezawa, 2006).…”

Section: Pendahuluanunclassified

“…Dalam regresi polinomial lokal tingkat kemulusan fungsinya ditentukan bandwidthnya. Penentuan bandwidth optimal dapat menggunakan metode GCV (Generalized Cross Validation) (Wu dan Zhang, 2006). Pada penelitian ini akan dimodelkan beban listrik di Kota Semarang menggunakan model Polinomial Lokal.…”

Section: Pendahuluanunclassified

“…Misalkan 0 x merupakan titik awal yang ditentukan dimana fungsi η akan diestimasi dengan estimator Kernel. Melalui deret Taylor, ( ) i x η pada persamaan (2) dapat didekati secara lokal oleh polinomial berderajat p sebagai berikut (Wu dan Zhang, 2006):…”

Section: Polinomial Lokalunclassified

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Pemodelan Regresi Nonparametrik Menggunakan Pendekatan Polinomial Lokal Pada Beban Listrik Di Kota Semarang

Suparti¹,

Prahutama²

2017

Medstat

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Semarang is the provincial capital of Central Java, with infrastructure and economic's growth was high. The phenomenon of power outages that occurred in Semarang, certainly disrupted economic development in Semarang. Large electrical energy consumed by industrial-scale consumers and households in the San Francisco area, monitored or recorded automatically and presented into a historical data load power consumption. Therefore, this study modeling the load power consumption at a time when not influenced by the use of electrical load (t-1)-th. Modeling using nonparametric regression approach with Local polynomial. In this study, the kernel used is a Gaussian kernel. In local polynomial modeling, determined optimum bandwidth. One of the optimum bandwidth determination using the Generalized Cross Validation (GCV). GCV values obtained amounted to 1425.726 with a minimum bandwidth of 394. Modelling generate local polynomial of order 2 with MSE value of 1408.672.

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Section: Pendahuluanunclassified

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Pemodelan Regresi Nonparametrik Menggunakan Pendekatan Polinomial Lokal Pada Beban Listrik Di Kota Semarang

Suparti¹,

Prahutama²

2017

Medstat

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“…The first predictor variable 1it x as the observation time (design time points) (Wu and Zhang, 2006) and f is the regression curve relationship between the predictor variables with the response variable y for toi subject. The curve f in nonparametric regression approach is used when the shape of the curve f is unknown (Eubank, 1999).…”

Section: Introductionmentioning

confidence: 99%

“…Spline estimator is one of the most commonly used estimator in nonparametric regression because it has a good visual interpretation, high flexibility and able to handle smooth function characters (Eubank, 1999). Regression curve f in spline estimator for longitudinal data used is assumed smooth, meaning that it is contained in a certain function space, especially Sobolev space or as written (Wu and Zhang, 2006).…”

Section: Introductionmentioning

confidence: 99%

Spline Estimator for Bi-Responses and Multi-Predictors Nonparametric Regression Model in Case of Longitudinal Data

Fernandes¹,

Budiantara²,

Otok³

et al. 2015

Journal of Mathematics and Statistics

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Nonparametric regression approach is used when the shape of the curve regression is unknown. The spline estimator approach for longitudinal data can accommodate the correlation between observations within the same subject, which is not found in the cross-section data, so that the autocorrelation assumption problem can be resolved. On the other hand, with bi-responses approach, it will accommodate any correlation between each response variables. The purposes of this study are (1) to obtain the function form of the nonparametric bi-responses and multipredictorsregression on longitudinal data, (2) to obtain the spline estimator in estimating the nonparametric bi-responses and multipredictorsregression curve on longitudinal data and (3) to apply the spline estimator in estimating the curve of nonparametric bi-responses and multi-predictorsregression on longitudinal data. Bi-responses and multipredictors nonparametric regression of the spline estimator on longitudinal data which meet the criteria of minimizing Penalized Weighted Least Square (PWLS). Application of data set (Patient in Pulmonary Tuberculosis) result shows that the spline estimator can be applied which gives the value of R 2 of 97.77%.

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Face and object cognition across adult age.

Hildebrandt

Wilhelm

Herzmann

et al. 2013

Psychology and Aging

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We investigated the specificity of face compared with object cognition from an individual differences and aging perspective by determining the amount of overlap between these abilities at the level of latent constructs across age. Confirmatory factor analytic models tested the specificity of speed and accuracy measures for face and object cognition (N = 448; 18 to 88 years). Accuracy measures were distinguishable and slightly dedifferentiated across age, which was not due to loss of visual acuity and contrast sensitivity. There was no face specificity for speed measures. These results support the specificity of face cognition from differential and developmental perspective only for performance accuracy.

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Nonparametric Regression Models for the Analysis of Longitudinal Data

Cited by 7 publications

References 68 publications

Pemodelan Regresi Nonparametrik Menggunakan Pendekatan Polinomial Lokal Pada Beban Listrik Di Kota Semarang

Pemodelan Regresi Nonparametrik Menggunakan Pendekatan Polinomial Lokal Pada Beban Listrik Di Kota Semarang

Spline Estimator for Bi-Responses and Multi-Predictors Nonparametric Regression Model in Case of Longitudinal Data

Face and object cognition across adult age.

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