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Articles de revues sur le sujet « Nonparametrica »

Auteur : Grafiati

Publié le 9 mars 2023

Mis à jour le 31 juillet 2025

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1

Firpha, Rosse Millania Pichago, and Anneke Iswani Achmad. "Regresi Nonparametrik Spline Truncated untuk Pemodelan Persentase Penduduk Miskin di Jawa Barat Pada Tahun 2021." Bandung Conference Series: Statistics 2, no. 2 (2022): 454–58. http://dx.doi.org/10.29313/bcss.v2i2.4720.

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Abstract. Nonparametric regression is a statistical method used to model the relationship between response variables and predictor variables whose pattern shape is unknown. In nonparametric regression there are several approaches, one of which is the spline. Spline nonparametric regression, if there is one predictor variable then the regression is called univariable spline nonparametric regression. Conversely, if there is one response variable with more than one predictor variable then the regression is called a multivariable spline nonparametric regression. In nonparametric regression there i

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Hallam, Arne. "A Brief Overview of Nonparametric Methods in Economics." Northeastern Journal of Agricultural and Resource Economics 21, no. 2 (1992): 98–112. http://dx.doi.org/10.1017/s0899367x00002610.

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The concept of nonparametric analysis, estimation, and inference has a long and storied existence in the annals of economic measurement. At least four rather distinct types of analysis are lumped under the broad heading of nonparametrics. The oldest, and perhaps most common, is that associated with distribution-free methods and order statistics. Similar in spirit, but different in emphasis, is nonparametric density estimation, such as the currently popular kernel estimator for regression. Semi-parametric or semi-nonparametric estimation combines parametric analysis of portions of the problem w

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Dani, Andrea Tri Rian, and Narita Yuri Adrianingsih. "Pemodelan Regresi Nonparametrik dengan Estimator Spline Truncated vs Deret Fourier." Jambura Journal of Mathematics 1, no. 1 (2021): 26–36. http://dx.doi.org/10.34312/jjom.v1i1.7713.

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ABSTRAKPendekatan regresi nonparametrik digunakan apabila hubungan antara variabel prediktor dan variabel respon tidak diketahui polanya. Spline truncated dan deret Fourier merupakan estimator dalam pendekatan nonparametrik yang terkenal, karena memiliki fleksibilitas yang tinggi dan mampu menyesuaikan terhadap sifat lokal data secara efektif. Penelitian ini bertujuan untuk mendapatkan estimator model regresi nonparametrik terbaik menggunakan spline truncated dan deret Fourier. Metode estimasi kurva regresi nonparametrik dilakukan dengan menyelesaikan optimasi Ordinary Least Squares (OLS). Kri

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Dani, Andrea Tri Rian, and Narita Yuri Adrianingsih. "Pemodelan Regresi Nonparametrik dengan Estimator Spline Truncated vs Deret Fourier." Jambura Journal of Mathematics 3, no. 1 (2021): 26–36. http://dx.doi.org/10.34312/jjom.v3i1.7713.

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ABSTRAKPendekatan regresi nonparametrik digunakan apabila hubungan antara variabel prediktor dan variabel respon tidak diketahui polanya. Spline truncated dan deret Fourier merupakan estimator dalam pendekatan nonparametrik yang terkenal, karena memiliki fleksibilitas yang tinggi dan mampu menyesuaikan terhadap sifat lokal data secara efektif. Penelitian ini bertujuan untuk mendapatkan estimator model regresi nonparametrik terbaik menggunakan spline truncated dan deret Fourier. Metode estimasi kurva regresi nonparametrik dilakukan dengan menyelesaikan optimasi Ordinary Least Squares (OLS). Kri

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Budiantara, I. Nyoman. "APLIKASI SPLINE ESTIMATOR TERBOBOT." Jurnal Teknik Industri 3, no. 2 (2004): 57–62. http://dx.doi.org/10.9744/jti.3.2.57-62.

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We considered the nonparametric regression model : Zj = X(tj) + ej, j = 1,2, ,n, where X(tj) is the regression curve. The random error ej are independently distributed normal with a zero mean and a variance s2/bj, bj > 0. The estimation of X obtained by minimizing a Weighted Least Square. The solution of this optimation is a Weighted Spline Polynomial. Further, we give an application of weigted spline estimator in nonparametric regression.   Abstract in Bahasa Indonesia :   Diberikan model regresi nonparametrik : Zj = X(tj) + ej, j = 1,2, ,n, dengan X (tj) kurv

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Lestari, Budi. "Estimasi Fungsi Regresi Dalam Model Regresi Nonparametrik Birespon Menggunakan Estimator Smoothing Spline dan Estimator Kernel." Jurnal Matematika Statistika dan Komputasi 15, no. 2 (2018): 20. http://dx.doi.org/10.20956/jmsk.v15i2.5710.

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Abstract Regression model of bi-respond nonparametric is a regression model which is illustrating of the connection pattern between respond variable and one or more predictor variables, where between first respond and second respond have correlation each other. In this paper, we discuss the estimating functions of regression in regression model of bi-respond nonparametric by using different two estimation techniques, namely, smoothing spline and kernel. This study showed that for using smoothing spline and kernel, the estimator function of regression which has been obtained in observation is a

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Safni Chusnaifah Junianingsih. "Regresi Nonparametrik Kernel dalam Pemodelan Jumlah Kelahiran Bayi di Jawa Barat Tahun 2017." Bandung Conference Series: Statistics 1, no. 1 (2021): 30–37. http://dx.doi.org/10.29313/bcss.v1i1.39.

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Abstract. Regression analysis is one of the analytical tools used to determine the effect of multiple predictor variables (X) on response variables (Y). The approach in regression analysis is divided into two, parametric approaches and nonparametric approaches. On nonparametric regression analysis, the shape of the regression curve is unknown, the data arega expected to look for its own estimation form so that it has high flexibility. Estimation of regression functions is performed with the Nadaraya Watson kernel estimator using Gaussian kernel functions. In this method requires bandwidth (h)

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Karimuse, William Yulius, Darnah Andi Nohe, and Meiliyani Siringoringo. "Pendekatan Regresi Nonparametrik Kernel pada Data IHSG Periode Januari 2020 – Desember 2021." STATISTIKA Journal of Theoretical Statistics and Its Applications 23, no. 1 (2023): 1–7. http://dx.doi.org/10.29313/statistika.v23i1.1628.

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ABSTRAK Pendekatan regresi nonparametrik Kernel digunakan untuk memperkirakan harapan bersyarat dari variabel dependen terhadap variabel independen tanpa mengasumsikan bentuk parametrik tertentu. Pendekatan ini menggunakan fungsi Kernel sebagai alat untuk melakukan estimasi. Dalam penelitian ini, digunakan fungsi Kernel Gaussian dan estimator Nadaraya-Watson. Estimator Nadaraya-Watson adalah metode yang mengestimasi fungsi regresi sebagai rata-rata tertimbang secara lokal, dengan menggunakan fungsi Kernel sebagai pembobot. Pendekatan regresi nonparametrik Kernel ini juga efektif dalam me

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Dani, Andrea Tri Rian, Narita Yuri Adrianingsih, Alifta Ainurrochmah, and Riry Sriningsih. "Flexibility of Nonparametric Regression Spline Truncated on Data without a Specific Pattern." Jurnal Litbang Edusaintech 2, no. 1 (2021): 37–43. http://dx.doi.org/10.51402/jle.v2i1.30.

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Bentuk pola hubungan antara variabel prediktor dan variabel respon ada yang diketahui, namun pada nyatanya ada pula yang tidak diketahui. Apabila bentuk pola hubungan antara variabel respon dan variabel prediktor tidak diketahui, pendekatan regresi nonparametrik merupakan pendekatan yang paling sesuai. Pendekatan regresi nonparametrik tidak tergantung pada asumsi bentuk kurva regresi tertentu, sehingga akan memberikan fleksibilitas yang tinggi. Salah satu estimator regresi nonparametrik yang terkenal adalah spline truncated. Spline truncated merupakan potongan-potongan polinomial yang memiliki

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Izumi, Sukma Wiyasih, and Teti Sofia Yanti. "Pemodelan Tingkat Pengangguran Terbuka (TPT) di Jawa Barat Menggunakan Regresi Nonparametrik Deret Fourier." Bandung Conference Series: Statistics 3, no. 2 (2023): 594–601. http://dx.doi.org/10.29313/bcss.v3i2.8769.

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Abstract. Regression analysis is a method used to determine the causal relationship of response variables with one or more predictor variables. It is generally divided into three regression analyses namely parametric, semiparametric, and nonparametric. Parametric regression has assumptions that can be met and the regression curve is known, but not all data can meet this. Therefore, the use of nonparametric regression can be an alternative because the use is not bound by assumptions and is used when the data does not follow a certain curve pattern. Nonparametric regression of the Fourier series

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Sanusi, Wahidah, Rahmat Syam, and Rabiatul Adawiyah. "Model Regresi Nonparametrik dengan Pendekatan Spline (Studi Kasus: Berat Badan Lahir Rendah di Rumah Sakit Ibu dan Anak Siti Fatimah Makassar)." Journal of Mathematics, Computations, and Statistics 2, no. 1 (2020): 70. http://dx.doi.org/10.35580/jmathcos.v2i1.12460.

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Pendekatan nonparametrik merupakan suatu pendekatan yang digunakan apabila bentuk hubungan antara variabel respon dan variabel prediktornya tidak diketahui atau tidak adanya informasi mengenai bentuk fungsi regresinya. Spline merupakan suatu teknik yang dilakukan untuk mengestimasi parameter dalam regresi nonparametrik. Penelitian ini bertujuan untuk mengetahui model hubungan antara berat badan lahir rendah dan faktor-faktor yang mempengaruhi berdasarkan model spline. Faktor-faktor tersebut adalah usia ibu, usia kehamilan, dan jarak kehamilan. Data tersebut diperoleh dari rumah sakit ibu dan a

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Ilmi, Hillidatul, Sifriyani, and Surya Prangga. "Geographically Weighted Spline Nonparametric Regression dengan Fungsi Pembobot Bisquare dan Gaussian Pada Tingkat Pengangguran Terbuka Di Pulau Kalimantan." J Statistika 14, no. 2 (2022): 84–92. http://dx.doi.org/10.36456/jstat.vol14.no2.a4470.

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Geographically weighted spline nonparametric regression merupakan pengembangan regresi nonparametrik untuk data spasial dengan estimator parameter bersifat lokal setiap lokasi pengamatan yang diaplikasikan pada kasus tingkat pengangguran terbuka. Tingkat pengangguran terbuka menjadi alat ukur kualitas kesejahteraan di suatu wilayah yang mengindikasikan besarnya persentase penduduk usia kerja yang aktif secara ekonomi. Tujuan penelitian ini yaitu untuk mengidentifikasi faktor-faktor yang mempengaruhi tingkat pengangguran terbuka 56 Kabupaten/Kota di Kalimantan. Metode yang digunakan adalah geog

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Rahayu, Nisrina Fajriati, and Lisnur Wachidah. "Regresi Nonparametrik Spline untuk Memodelkan Faktor-faktor yang Memengaruhi Indeks Pembangunan Gender (IPG) di Jawa Barat Tahun 2020." Bandung Conference Series: Statistics 2, no. 2 (2022): 273–81. http://dx.doi.org/10.29313/bcss.v2i2.4037.

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Abstract. Regression analysis is a statistical method used to determine the pattern of the relationship between the independent variable and the dependent variable. There are three kinds of regression analysis, namely parametric regression analysis, semiparametric regression analysis and nonparametric regression analysis. Parametric regression analysis can be used when the assumptions are met but not all data can meet the parametric assumptions, an alternative to parametric regression is nonparametric regression because its use does not require strict assumptions. Spline nonparametric regressi

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Budiantara, I. Nyoman, Fredi Suryadi, Bambang Widjanarko Otok, and Suryo Guritno. "PEMODELAN B-SPLINE DAN MARS PADA NILAI UJIAN MASUK TERHADAP IPK MAHASISWA JURUSAN DISAIN KOMUNIKASI VISUAL UK. PETRA SURABAYA." Jurnal Teknik Industri 8, no. 1 (2006): 1–13. http://dx.doi.org/10.9744/jti.8.1.1-13.

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Regression analysis is constructed for capturing the influences of independent variables to dependent ones. It can be done by looking at the relationship between those variables. This task of approximating the mean function can be done essentially in two ways. The quiet often use parametric approach is to assume that the mean curve has some prespecified functional forms. Alternatively, nonparametric approach, .i.e., without reference to a specific form, is used when there is no information of the regression function form (Haerdle, 1990). Therefore nonparametric approach has more flexibilities

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FITRIANI, ANNA, I. GUSTI AYU MADE SRINADI, and MADE SUSILAWATI. "ESTIMASI MODEL REGRESI SEMIPARAMETRIK MENGGUNAKAN ESTIMATOR KERNEL UNIFORM (Studi Kasus: Pasien DBD di RS Puri Raharja)." E-Jurnal Matematika 4, no. 4 (2015): 176. http://dx.doi.org/10.24843/mtk.2015.v04.i04.p108.

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Semiparametric regression model estimation is an estimation that combines both parametric and nonparametric regression model. In semiparametric regression, some of the variables are parametrics and the others are nonparametrics. Semiparametric regression is used when relationship pattern between independent and depentdent variables is half known and half unknown. Regression curve smoothing technique in nonparametric components in this study was using uniform kernel function. The optimal semiparametric regression curve estimation was obtained by optimal bandwidth. By choosing optimal bandwidth,

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Intan, Putroue Keumala. "Pemodelan Jumlah Rumah Tangga Sangat Miskin di Jawa Timur Menggunakan Regresi Nonparametrik B-Spline." Majalah Ilmiah Matematika dan Statistika 24, no. 2 (2024): 147. http://dx.doi.org/10.19184/mims.v24i2.37749.

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Indonesia is a developing country that continues to experience poverty. East Java is one of the provinces that ranks 3rd as the province with the largest number of poor people in Indonesia. This study aims to model the number of very poor households in East Java using the B-Spline nonparametric regression method. The results show that the B-Spline nonparametric regression model for the number of very poor households in East Java only uses four independent variables, namely the number of households with drinking water sources from mineral water, the number of households that do not use electric

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Sri Mulyani, Dea, and Abdul Kudus. "Penerapan Regresi Nonparametrik Smooting Spline untuk Data Tersensor dalam Memodelkan Hubungan Antara Lamanya Waktu Kesembuhan Rawat Inap Pada Pasien Diabetes Melitus Tipe-2 dengan Usia Pasien." Bandung Conference Series: Statistics 3, no. 2 (2023): 625–32. http://dx.doi.org/10.29313/bcss.v3i2.8966.

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Abstract. The aim of study is to explain the case. Regression analysis is used to model or look for patterns of relationship between one or more independent variables and one or more response variables. Since the data often does not follow a specific formulation pattern, a more flevible model is required, namely nonparametric regression model approach is an approachused when the shape of the shape of the relationship between the response variable and the independent variable is unknown or information about the shape of the regression function is not available. The nonparametric spline regressi

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Linton, Oliver B., and Yang Yan. "Semi- and Nonparametric ARCH Processes." Journal of Probability and Statistics 2011 (2011): 1–17. http://dx.doi.org/10.1155/2011/906212.

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ARCH/GARCH modelling has been successfully applied in empirical finance for many years. This paper surveys the semiparametric and nonparametric methods in univariate and multivariate ARCH/GARCH models. First, we introduce some specific semiparametric models and investigate the semiparametric and nonparametrics estimation techniques applied to: the error density, the functional form of the volatility function, the relationship between mean and variance, long memory processes, locally stationary processes, continuous time processes and multivariate models. The second part of the paper is about t

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Ahmad, Firda Balqisa, and Suliadi. "Diagram Kendali Nonparamterik Composite Exponentially Weighted Moving Average (CEWMA) Sign dalam Proses Plating Aksesori Mobil PT. XYZ." Bandung Conference Series: Statistics 3, no. 2 (2023): 643–52. http://dx.doi.org/10.29313/bcss.v3i2.9054.

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Abstract. Control charts are one of the tools in Statistical Process Control (SPC) that are graphically used to control the production process. The first control chart to exist was the Shewhart control chart, but the control chart is not sensitive in detecting small shifts. So the alternative is the EWMA control chart which is able to detect small shifts, but there is still a problem because this diagram has the assumption that the observation process is normally distributed. Then a Nonparametric EWMA Sign control chart is proposed which can detect small shifts when the data conditions are not

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Al Azies, Harun, and Dea Trishnanti. "Pemodelan Pengaruh Imunisasi DPT Terhadap Angka Kematian Bayi di Jawa Timur Tahun 2016 Menggunakan Pendekatan Regresi Nonparametrik Spline." J Statistika: Jurnal Ilmiah Teori dan Aplikasi Statistika 12, no. 1 (2019): 26–31. http://dx.doi.org/10.36456/jstat.vol12.no1.a1995.

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East Java is one of the provinces with a high IMR level. Based on the District / City report in East Java, in 2006 it was 0.035 live births and became 0.0032 live births in 2008. Identification of factors that influence both indicators correctly can be done by modeling, namely by nonparametric regression analysis. The nonparametric regression approach used is Spline, with its strengths the model tends to look for estimates wherever the data moves. This is because there is a knot point which is a joint fusion point which indicates a change in data behavior patterns. Based on the results of anal

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Side, Syafruddin, Wahidah Sanusi, and Mustati'atul Waidah Maksum. "Model Regresi Semiparametrik Spline untuk D ata Longitudinal pada Kasus Demam Berdarah Dengue di Kota Makassar." Journal of Mathematics Computations and Statistics 3, no. 1 (2021): 20. http://dx.doi.org/10.35580/jmathcos.v3i1.19181.

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Abstrak. Regresi semiparametrik merupakan model regresi yang memuat komponen parametrik dan komponen nonparametrik dalam suatu model. Pada penelitian ini digunakan model regresi semiparametrik spline untuk data longitudinal dengan studi kasus penderita Demam Berdarah Dengue (DBD) di Rumah Sakit Universitas Hasanuddin Makassar periode bulan Januari sampai bulan Maret 2018. Estimasi model regresi terbaik didapat dari pemilihan titik knot optimal dengan melihat nilai Generalized Cross Validation (GCV) dan Mean Square Error (MSE) yang minimum. Komponen parametrik pada penelitian ini adalah hemoglo

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Muliere, Pietro, and Marco Scarsini. "Change-point problems: A Bayesian nonparametric approach." Applications of Mathematics 30, no. 6 (1985): 397–402. http://dx.doi.org/10.21136/am.1985.104169.

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A. Taha, Taha, and Sufyan Abdulraheem Mutar. "Nonparametric Survival Function for Pneumo Carcinoma Patients." International Journal of Science and Research (IJSR) 12, no. 9 (2023): 245–50. http://dx.doi.org/10.21275/sr23820180940.

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Putri Ratna Wulan and Nur Azizah Komara Rifai. "Penerapan Regresi Nonparametrik B-Spline pada Model Tingkat Pengangguran Terbuka Berdasarkan Tingkat Partisipasi Angkatan Kerja dan PDRB di Provinsi Jawa Barat." Bandung Conference Series: Statistics 3, no. 2 (2023): 294–302. http://dx.doi.org/10.29313/bcss.v3i2.8095.

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Abstract. The main problem in performing regression analysis is getting an estimate of the shape of the regression curve. There are several approaches in regression models, namely parametric, nonparametric and semiparametric regression models. The shape of the regression model depends on the curve . Non-parametric regression is a method to determine the pattern of the relationship between predictor variables and response variables whose function form is unknown. This is because there is no prior information about the shape of . B-Spline is one of the nonparametric regression methods. B-Spline

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Islamiyati, Anna, Anisa Anisa, Raupong Raupong, et al. "Estimasi Model Regresi Spline Kubik Tersegmen dengan Metode Penalized Least Square." Al-Khwarizmi : Jurnal Pendidikan Matematika dan Ilmu Pengetahuan Alam 10, no. 2 (2022): 139–48. http://dx.doi.org/10.24256/jpmipa.v10i2.3197.

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Abstract:Nonparametric regression is used for data whose data pattern is non-parametric. One of the estimators that can be developed is a segmented cubic spline which is able to show several segmentation changes in the data. This article examines the estimation of segmented cubic spline nonparametric regression models using the Penalized Least Square estimation criteria. The method involves knot points and smoothing parameters simultaneously. In addition, the model is used to analyze data on BPJS claims based on patient age. The results show that the optimal model is at two-knot points, namely

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Cooper, Joseph C. "Nonparametric and Semi‐Nonparametric Recreational Demand Analysis." American Journal of Agricultural Economics 82, no. 2 (2000): 451–62. http://dx.doi.org/10.1111/0002-9092.00038.

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Theofani, Eukaristianica, and Ike Herdiana. "Meningkatkan resiliensi penyintas pelesual melalui terapi pemaafan." Jurnal Ilmiah Psikologi Terapan 8, no. 1 (2020): 1. http://dx.doi.org/10.22219/jipt.v8i1.9865.

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Abstrak. Kehamilan yang tidak diinginkan akibat pelecehan seksual terasa sangat berat bagi wanita sehingga dibutuhkan kemampuan untuk bangkit dari keterpurukan yang disebut resiliensi. Individu yang resilien mampu menghadapi hal yang menekan dalam hidupnya dan berusaha untuk mengatasi tekanan melalui strategi koping, salah satunya dengan melakukan pemaafan. Terapi pemaafan adalah salah satu bentuk intervensi yang dapat meningkatkan resiliensi. Tujuan penelitian ini adalah untuk mengetahui peningkatan resiliensi pada wanita penyintas pelecehan seksual melalui terapi pemaafan. Penelitian ini men

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Rahayu, Alfin, Rachmadania Akbarita, and Risang Narendra. "Analisis Pengaruh Gender Terhadap Indeks Pembangunan Gender Menggunakan Regresi Campuran Nonparametrik Spline Linier Truncated dan Fungsi Kernel." Jurnal Telematika 16, no. 1 (2021): 33–39. http://dx.doi.org/10.61769/telematika.v16i1.391.

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This research is motivated by the many gender deviations in Blitar Regency in the last ten years that men dominate more than women, both in work, education, and politics. This situation causes gender development to be important by creating equality between men and women in Blitar Regency. The method to analyze the effect of gender on the Gender Development Index used in this study is Nonparametric Spline Linear Truncated Mixed Regression and Kernel Function with eight predictor variables. The results obtained from the nonparametric Spline Linear Truncated Mixed Regression model and the Kernel

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Epifani, Ilenia. "Bayesian Nonparametrics." Journal of the American Statistical Association 99, no. 467 (2004): 898–99. http://dx.doi.org/10.1198/jasa.2004.s346.

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Shang, Han Lin. "Bayesian Nonparametrics." Journal of Applied Statistics 38, no. 12 (2011): 2990. http://dx.doi.org/10.1080/02664763.2011.559374.

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Belcher, John. "Nonparametric methods." Nurse Researcher 9, no. 1 (2001): 17–25. http://dx.doi.org/10.7748/nr2001.10.9.1.17.c6172.

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Krzywinski, Martin, and Naomi Altman. "Nonparametric tests." Nature Methods 11, no. 5 (2014): 467–68. http://dx.doi.org/10.1038/nmeth.2937.

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Racine, Jeffrey S. "Nonparametric Econometrics." Journal of the American Statistical Association 96, no. 453 (2001): 339–55. http://dx.doi.org/10.1198/jasa.2001.s374.

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Ueda, Naonori. "Nonparametric Bayes." Journal of The Institute of Image Information and Television Engineers 70, no. 5 (2016): 478–80. http://dx.doi.org/10.3169/itej.70.478.

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Brown, George W., and Gregory F. Hayden. "Nonparametric Methods." Clinical Pediatrics 24, no. 9 (1985): 490–98. http://dx.doi.org/10.1177/000992288502400905.

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Dias, Ronaldo. "Nonparametric econometrics." Brazilian Review of Econometrics 22, no. 1 (2002): 127. http://dx.doi.org/10.12660/bre.v22n12002.2747.

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Linton, O. B. "Nonparametric econometrics." Journal of Statistical Planning and Inference 92, no. 1-2 (2001): 299–300. http://dx.doi.org/10.1016/s0378-3758(00)00070-7.

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Lehmkuhl, L. Don. "Nonparametric Statistics." JPO Journal of Prosthetics and Orthotics 8, no. 3 (1996): 24A. http://dx.doi.org/10.1097/00008526-199607000-00008.

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Pettitt, A. N., P. R. Krishnaiah, and P. K. Sen. "Nonparametric Methods." Journal of the Royal Statistical Society. Series A (General) 150, no. 2 (1987): 172. http://dx.doi.org/10.2307/2981642.

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Ozertem, Umut, and Deniz Erdogmus. "Nonparametric Snakes." IEEE Transactions on Image Processing 16, no. 9 (2007): 2361–68. http://dx.doi.org/10.1109/tip.2007.902335.

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Praagman, J. "Nonparametric methods." European Journal of Operational Research 28, no. 2 (1987): 238–39. http://dx.doi.org/10.1016/0377-2217(87)90234-7.

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Chou, Chien-fu, and Gabriel Talmain. "Nonparametric search." Journal of Economic Dynamics and Control 17, no. 5-6 (1993): 771–84. http://dx.doi.org/10.1016/0165-1889(93)90014-j.

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Pandis, Nikolaos. "Nonparametric methods." American Journal of Orthodontics and Dentofacial Orthopedics 148, no. 4 (2015): 695. http://dx.doi.org/10.1016/j.ajodo.2015.07.014.

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Shott, S. "Nonparametric statistics." Journal of the American Veterinary Medical Association 198, no. 7 (1991): 1126–28. http://dx.doi.org/10.2460/javma.1991.198.07.1126.

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Grama, Ion G., and Michael H. Neumann. "Asymptotic equivalence of nonparametric autoregression and nonparametric regression." Annals of Statistics 34, no. 4 (2006): 1701–32. http://dx.doi.org/10.1214/009053606000000560.

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Ali Fadhil Abduljabbar and Afrah Mohammed Kadhim. "Comparison the Robust Estimators Nonparametric of Nonparametric Regressions." Tikrit Journal of Pure Science 28, no. 1 (2023): 96–100. http://dx.doi.org/10.25130/tjps.v28i1.1271.

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In order to get rid of or reduce the abnormal values of some phenomena that may be the reason for not obtaining the desired results. This makes us to get conclusions far from reality for the phenomenon we are studying. That the traditional nonparametric estimators are very sensitive to anomalous values, which prompted us to use the fortified estimators because they are not much affected by the anomalous values, as well as the nonparametric regression because it does not depend on the previous determinants or assumptions, but it depends directly and fundamentally on the data.

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Lin Jiarui, 林嘉睿, 孙佳蕾 Sun Jialei, 张饶 Zhang Rao, 郑书彦 Zheng Shuyan та 邾继贵 Zhu Jigui. "大尺度线结构激光面的非参数模型标定方法". Acta Optica Sinica 41, № 16 (2021): 1612001. http://dx.doi.org/10.3788/aos202141.1612001.

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Vincent, Odhiambo, Hellen Waititu, and Nyakundi Omwando Cornelious. "Nonparametric Estimation of Error Variance under Simple Random Sampling without Replacement." International Journal of Mathematics And Computer Research 10, no. 10 (2022): 2925–33. http://dx.doi.org/10.47191/ijmcr/v10i10.02.

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This study adopts a nonparametric approach in the estimation of a finite population error variance in the setting where the variance is a constant (homoscedastic) using a model-based technique under simple random sampling without replacement (SRSWOR). A mean square analysis of the estimator has been conducted, including the asymptotic behaviour of the estimator and the results show that the asymptotic distribution in a homoscedastic setting is asymptotically unbiased and consistent. The performance of the developed estimator is compared to that of other existing estimators using real data. R s

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de Jesus Moreno, Jose, Aldo Rafael Sartorius Castellanos, Marcia Lorena Hernandez Nieto, Antonia Zamudio Radilla, and Bryan Quino Ortiz. "Spectral Analysis using Nonparametric Techniques to Identify Movements in Encephalographies Signals." International Journal of Scientific Engineering and Research 10, no. 11 (2022): 1–10. https://doi.org/10.70729/se221104104845.

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Subhan, Muhammad, Ainun Mahmuda, and Eka Filahanasari. "PENGARUH MODEL PEMBELAJARAN MIND MAPPING TERHADAP HASIL BELAJAR MATEMATIKA PADA MATERI BANGUN DATAR KELAS IV SDN 09 SITIUNG." Jurnal IKA PGSD (Ikatan Alumni PGSD) UNARS 13, no. 1 (2023): 25. http://dx.doi.org/10.36841/pgsdunars.v13i1.3046.

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Tujuan dari penelitian ini adalah untuk dapat mengetahui pengaruh dari model pembelajaran mind mapping terhadap hasil belajar matematika siswa kelas IV khususnya pada materi bangun datar. Permasalahan yang melatarbelakangi dalam penelitian ini adalah rendahnya hasil belajar matematika kelas IV SDN 09 Sitiung. Upaya dalam mengatasi permasalahan tersebut yaitu dengan menerapkan model pembelajaran mind mapping pada pada mata pelajaran matematika dengan materi bangun datar. Pada penelitian ini menggunakan jenis Pre-Experimental design dengan desain penelitian yaitu One-Grup-Pretest-Posttest Design

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