Relevant bibliographies by topics / Nonparametrica

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Academic literature on the topic 'Nonparametrica'

Author: Grafiati
Published: 9 March 2023
Last updated: 10 March 2023

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Journal articles on the topic "Nonparametrica"

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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 is a truncated spline model. The truncated spline function is a polynomial function that is dismembered at a knot point. A knot point is a joint fusion point where the function is truncated, or a point that describes a change in data behavior at a certain sub-sub-interval. Therefore, truncated spline models have an excellent ability to handle data whose behavior is arbitrary at certain sub-sub intervals. This study will use truncated spline nonparametric regression to model the number of poor people in West Java in 2021. The data used is secondary data sourced from the publication of the Indonesian Central Statistics Agency (BPS).
 Abstrak. Regresi nonparametrik adalah suatu metode statistika yang digunakan untuk memodelkan hubungan antara variabel respon dengan variabel prediktor yang tidak diketahui bentuk polanya. Dalam regresi nonparametrik terdapat beberapa pendekatan salah satunya spline. Regresi nonparametrik spline, jika terdapat satu variabel prediktor maka regresi tersebut dinamakan regresi nonparametrik spline univariabel. Sebaliknya, apabila terdapat satu variabel respon dengan lebih dari satu variabel prediktor maka regresi tersebut disebut regresi nonparametrik spline multivariable. Dalam regresi nonparametrik terdapat model Spline Truncated. Fungsi Spline Truncated merupakan fungsi polinomial yang terpotong-potong pada suatu titik knot. Titik knot merupakan titik perpaduan bersama dimana fungsi tersebut terpotong, atau titik yang menggambarkan terjadinya perubahan perilaku data pada sub-sub interval tertentu. Oleh karena itu, model Spline Truncated memiliki kemampuan yang sangat baik untuk menangani data yang perilakunya berubah-ubah pada sub-sub interval tertentu. Pada penelitian ini akan menggunakan regresi nonparametrik Spline Truncated untuk mememodelkan jumlah penduduk miskin di Jawa Barat pada tahun 2021. Data yang digunakan adalah data sekunder yang bersumber dari publikasi Badan Pusat Statistika (BPS) Indonesia.
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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 with nonparametric specification for the remainder, such as the specification of a specific functional form for a regression function with a nonparametric representation of the error distribution. The final type of nonparametrics is that associated with data envelopment analysis and revealed preference, although the use of the term nonparametrics for this research is perhaps a misnomer. This paper will briefly review each of the four types of analysis, leaning heavily on other published work for more detailed exposition. The paper will then discuss in more detail the application of the revealed-preference approach to four specific economic problems: efficiency, the structure of technology or preferences, technical or taste change, and risky choice. The paper is not complete, exhaustive, or detailed. The primary purpose is to expose the reader to a variety of techniques and provide ample reference to the relevant literature.
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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). Kriteria kebaikan model menggunakan GCV, R2 dan MSE. Pemodelan regresi nonparametrik diterapkan pada data Case Fatality Rate (CFR) akibat Demam Berdarah Dengue (DBD) di Indonesia. Berdasarkan hasil analisis, hasil estimasi dari pemodelan regresi nonparametrik menunjukkan bahwa estimator spline truncated memberikan performa yang lebih baik dibandingkan estimator deret Fourier. Hal ini ditunjukkan dengan nilai R2 dari estimator spline truncated yaitu sebesar 91,80% dan MSE sebesar 0,04, sedangkan dengan estimator deret Fourier diperoleh nilai R2 sebesar 65,44% dan MSE sebesar 0,19.ABSTRACTThe nonparametric regression approach is used when the relationship between the predictor variable and the response variable is unknown. Spline truncated and Fourier series are well-known estimators in the nonparametric approach because they have high flexibility and are able to adjust to the local properties of the data effectively. This study aims to obtain the best nonparametric regression model estimator using the truncated spline and the Fourier series. The nonparametric regression curve estimation method is done by completing the Ordinary Least Squares (OLS) optimization. The criteria for the goodness of the model use GCV, R2, and MSE. Nonparametric regression modeling is applied to Case Fatality Rate (CFR) modeling due to Dengue Hemorrhagic Fever (DBD) in Indonesia. Based on the analysis, the estimation results from the nonparametric regression modeling show that the truncated spline estimator provides better performance than the Fourier series estimator. This is shown by the R2 value of the truncated spline estimator which is 91.80% and the MSE is 0.04, while the Fourier series estimator obtained an R2 value of 65.44% and MSE of 0.19.
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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). Kriteria kebaikan model menggunakan GCV, R2 dan MSE. Pemodelan regresi nonparametrik diterapkan pada data Case Fatality Rate (CFR) akibat Demam Berdarah Dengue (DBD) di Indonesia. Berdasarkan hasil analisis, hasil estimasi dari pemodelan regresi nonparametrik menunjukkan bahwa estimator spline truncated memberikan performa yang lebih baik dibandingkan estimator deret Fourier. Hal ini ditunjukkan dengan nilai R2 dari estimator spline truncated yaitu sebesar 91,80% dan MSE sebesar 0,04, sedangkan dengan estimator deret Fourier diperoleh nilai R2 sebesar 65,44% dan MSE sebesar 0,19.ABSTRACTThe nonparametric regression approach is used when the relationship between the predictor variable and the response variable is unknown. Spline truncated and Fourier series are well-known estimators in the nonparametric approach because they have high flexibility and are able to adjust to the local properties of the data effectively. This study aims to obtain the best nonparametric regression model estimator using the truncated spline and the Fourier series. The nonparametric regression curve estimation method is done by completing the Ordinary Least Squares (OLS) optimization. The criteria for the goodness of the model use GCV, R2, and MSE. Nonparametric regression modeling is applied to Case Fatality Rate (CFR) modeling due to Dengue Hemorrhagic Fever (DBD) in Indonesia. Based on the analysis, the estimation results from the nonparametric regression modeling show that the truncated spline estimator provides better performance than the Fourier series estimator. This is shown by the R2 value of the truncated spline estimator which is 91.80% and the MSE is 0.04, while the Fourier series estimator obtained an R2 value of 65.44% and MSE of 0.19.
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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 regression linier. In addition, both estimators that are obtained from those two techniques are systematically only different on smoothing matrices. Keywords: kernel estimator, smoothing spline estimator, regression function, bi-respond nonparametric regression model. AbstrakModel regresi nonparametrik birespon adalah suatu model regresi yang menggambarkan pola hubungan antara dua variabel respon dan satu atau beberapa variabel prediktor dimana antara respon pertama dan respon kedua berkorelasi. Dalam makalah ini dibahas estimasi fungsi regresi dalam model regresi nonparametrik birespon menggunakan dua teknik estimasi yang berbeda, yaitu smoothing spline dan kernel. Hasil studi ini menunjukkan bahwa, baik menggunakan smoothing spline maupun menggunakan kernel, estimator fungsi regresi yang didapatkan merupakan fungsi linier dalam observasi. Selain itu, kedua estimator fungsi regresi yang didapatkan dari kedua teknik estimasi tersebut secara matematis hanya dibedakan oleh matriks penghalusnya.Kata Kunci : Estimator Kernel, Estimator Smoothing Spline, Fungsi Regresi, Model Regresi Nonparametrik Birespon.
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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) or finer parameters as a balance controller between the smoothness of the function and the suitability of the function of the data. Optimum bandwidth (h) is obtained by minimizing the Generalized Cross Validation (GCV) value. Based on the analysis, obtained in a simple linear regression model obtained a Mean Square Error (MSE) value of 552976772 and a Standard Error (SE) of 24437,98. While in the kernel nonparametric regression model, the optimum bandwidth (h) is 0,50, Mean Square Error (MSE) is 96832714, and the Standard Error (SE) value is 10226,4. So it can be concluded that the kernel nonparametric regression model is better than a simple linear regression model.
 Abstrak. Analisis regresi merupakan salah satu alat analisis yang digunakan untuk mengetahui pengaruh dari beberapa variabel prediktor (X) terhadap variabel respon (Y). Pendekatan dalam analisis regresi dibagi menjadi dua, yaitu pendekatan parametrik dan pendekatan nonparametrik. Pada analisis regresi nonparametrik bentuk kurva regresi tidak diketahui, data diharapkan mencari sendiri bentuk estimasinya sehingga memiliki fleksibilitas yang tinggi. Estimasi fungsi regresi dilakukan dengan estimator kernel Nadaraya Watson menggunakan fungsi kernel Gaussian. Metode ini membutuhkan bandwidth (h) atau parameter penghalus sebagai pengontrol keseimbangan antara kemulusan fungsi dan kesesuaian fungsi terhadap data. Bandwidth (h) optimum diperoleh dengan meminimumkan nilai Generalized Cross Validation (GCV). Berdasarkan analisis diperoleh pada model regresi linear sederhana diperoleh nilai Mean Square Error (MSE) sebesar 552976772 dan niai Standard Error (SE) sebesar 24437,98. Sedangkan pada model regresi nonparametrik kernel diperoleh bandwidth (h) optimum sebesar 0,50, Mean Square Error (MSE) sebesar 96832714, dan nilai Standard Error (SE) sebesar 10226,4. Sehingga dapat disimpulkan bahwa model regresi nonparametrik kernel lebih baik daripada model regresi linear sederhana.
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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 sifat tersegmen dan kontinu. Pada penelitian ini, akan disimulasikan pola hubungan antara kedua variabel yaitu respon dan prediktor yang tidak memiliki pola tertentu, yang kemudian didekati dengan dua pendekatan regresi, yaitu parametrik dan nonparametrik. Berdasarkan ukuran kebaikan estimasi kurva regresi menggunakan koefisien determinasi diperoleh hasil bahwa pendekatan regresi nonparametrik lebih baik daripada pendekatan regresi parametrik. Hal ini dikarenakan pendekatan regresi nonparametric memiliki fleksibilitas yang tinggi sehingga mampu menyesuaikan sendiri bentuk estimasi kurva regresi.
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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 anak siti Fatimah Makassar tahun 2017. Dimana untuk mendapatkan model spline terbaik langkah awal yang dilakukan adalah menentukan knot dengan nilai Generalized Cross Validation (GCV) yang minimum. Berdasarkan penelitian yang telah dilakukan, dua variabel dinyatakan berpengaruh terhadap berat badan lahir rendah yaitu usia ibu, dan usia kehamilan. Model regresi nonparametrik dengan pendekatan Spline yang terbentuk memiliki koefisien determinasi sebesar 78,19%, serta nilai GCV dengan tiga titik knot yaitu 0.0117.Kata kunci: Regresi Nonparametrik, Spline, Berat Badan Lahir Rendah, Generalized Cross Validation The non-parametric approach is an approach that is used if the form of the relationship between the response variable and the predictor variable is unknown or the absence of information about the shapes of regression functions. The Spline is a technique performed to estimate the parameters in the nonparametric regression. This study aims to determine the model of the relationship between low birth weight and the factors that affect the based on the spline model. Such factors are maternal age, gestational age, and pregnancy distance. The Data is obtained from the mother and child hospital siti Fatimah Makassar 2017. Where to get a spline model best the initial step is to determine the knots with the value of the Generalized Cross Validation (GCV) which is a minimum. Based on the research that has been done, the two variables stated effect against low birth weight, namely age of mother, and gestational age. Nonparametric regression Model with the approach of the Spline that is formed has a coefficient of determination of 78.19 to%, as well as the value of the GCV with a three-point knot that is 0.0117.Keyword : Nonparametric Regression, Spline, Low Birth Weight, Generalized Cross Validation
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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 geographically weighted spline nonparametric regression dengan pembobot fungsi kernel eksponensial. Model terbaik geographically weighted spline nonparametric regression dengan pembobot fungsi kernel eksponensial pada orde 1 titik knot 1 dengan nilai R-Square sebesar 86,410 persen, nilai AIC sebesar 12,152, nilai RMSE sebesar 0,584 serta nilai CV terkecil adalah fungsi kernel bisquare sebesar 77,175. Adapun faktor-faktor yang berpengaruh signifikan terhadap tingkat pengangguran terbuka yaitu tingkat partisipan angkatan kerja, jumlah penduduk, indeks pembangunan manusia, harapan lama sekolah dan upah minimum.
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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 regression is a method used to get the estimated regression curve through the estimation of the data pattern according to the movement. The selection of the best model for Spline regression is seen from the Generalized Cross Validiation (GCV) value using the minimum knot point. In this study, the dependent variable used is the Gender Development Index (GDI) in West Java Province in 2020 which consists of 18 districts and 9 cities with the independent variables consisting of the average length of schooling for women, the expected length of schooling for women, the open unemployment rate for women, female labor force participation rate, women with health complaints and sex ratio. The results of the analysis obtained that the best nonparametric Spline regression model was using the order of one and three knot points with the minimum GCV value of 0.2471, and the coefficient of determination was 99.98%. The six independent variables used have a significant influence on GPA in West Java in 2020.
 Abstrak. Analisis regresi adalah metode statistika yang digunakan untuk menentukan pola hubungan antara variabel bebas dengan variabel terikat. Terdapat tiga macam analisis regresi, yaitu analisis regresi parametrik, analisis regresi semiparametrik dan analisis regesi nonparametrik. Analisis regresi parametrik dapat digunakan ketika asumsi terpenuhi akan tetapi tidak semua data dapat memenuhi asumsi parametrik, alternatif dari regresi parametrik adalah regresi nonparametrik karena penggunaanya tidak memerlukan asumsi yang ketat. Regresi nonparametrik Spline merupakan metode yang digunakan untuk mendapatkan dugaan kurva regresi melalui pendekatan estimasi pola data sesuai pergerakannya. Pemilihan model terbaik pada regesi Spline dilihat dari nilai Generalized Cross Validiation (GCV) dengan menggunakan titik knot yang paling minimum. Pada penelitian ini variabel terikat yang digunakan adalah Indeks Pembangunan Gender (IPG) di Provinsi Jawa Barat Tahun 2020 yang terdiri dari 18 kabupaten dan 9 kota dengan variabel bebas yang terdiri dari rata-rata lama sekolah perempuan, harapan lama sekolah perempuan, tingkat pengangguran terbuka perempuan, tingkat partisipasi angkatan kerja perempuan, perempuan yang memiliki keluhan kesehatan dan rasio jenis kelamin. Hasil dari analisis diperoleh model regresi nonparametrik Spline yang terbaik adalah dengan menggunakan orde satu dan tiga titik knot dengan nilai GCV yang paling minimum 0,2471, serta didapatkan nilai koefisien determinasi sebesar 99,98%. Dengan ke enam variabel bebas yang digunakan memiliki pengaruh yang signifikan terhadap IPG di Jawa Barat tahun 2020.
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Dissertations / Theses on the topic "Nonparametrica"

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CORRADIN, RICCARDO. "Contributions to modelling via Bayesian nonparametric mixtures." Doctoral thesis, Università degli Studi di Milano-Bicocca, 2019. http://hdl.handle.net/10281/241261.

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I modelli mistura in ambito Bayesiano nonparametrico sono modelli flessibili per stime di densità e clustering, ormai uno strumento di uso comune in ambito statistico applicato. Il primo modello introdotto in questo ambito è stato il processo di Dirichlet (DP) (Ferguson, 1973) combinato con un kernel Gaussiano(Lo, 1984). Recentemente è cresciuto l’interesse verso la definizione di modelli mistura basati su misure nonparametriche che generalizzano il DP. Tra le misure proposte, il processo di Pitman-Yor (PY) (Perman et al., 1992; Pitman, 1995) e, più in generale, la classe di Gibbs-type prior (see e.g. De Blasi et al., 2015) rappresentano generalizzazioni convenienti in grado di combinare trattabilità matematica, interpretabilità e flessibilità. In questa tesi investighiamo tre aspetti dei modelli mistura nonparametrici, in ordine, proprietà dei modelli, aspetti computazionali e proprietà distributive. La tesi è organizzata come segue. Il primo capitolo propone una revisione coincisa della statistica Bayesiana nonparametrica, con particolare attenzione a strumenti e modelli utili nei capitoli successivi. Introduciamo le nozioni di scambiabilità, partizioni scambiabili e random probability measure. Discutiamo quindi alcuni casi particolari, i processi DP e PY, ingredienti principali rispettivamente nel secondo e nel terzo capitolo. Infine discutiamo brevemente la logica dietro la definizione di classi più generali di priors nonparametriche discrete. Nel secondo capitolo proponiamo uno studio dell’effetto di trasformazioni affini invertibili dei dati sulla distribuzione a posteriori di modelli mistura DP, con particolare attenzione ai modelli con kernel Gaussiano (DPM-G). Introduciamo un risultato riguardante la specificazione dei parametri di un modello in relazione a trasformazioni dei dati. Successivamente formalizziamo la nozione di robustezza asintotica di un modello nel caso di trasformazioni affini dei dati e dimostriamo un risultato asintotico che, basandosi sulla consistenza asintotica di modelli DPM-G, mostra che, sotto alcune assunzioni sulla distribuzione che ha generato i dati, i modelli DPM-G sono asintoticamente robusti. Nel terzo capitolo presentiamo l’Importance Conditional Sampler (ICS), un nuovo schema di campionamento condizionale per modelli mistura PY, basato su una rappresentazione della distribuzione a posteriori di un processo PY (Pitman, 1996) e sull’idea di importance sampling, ispirandosi al passo augmentation del noto Algoritmo 8 di Neal (2000). Il metodo proposto combina convenientemente le migliori caratteristiche dei metodi esistenti, condizionali e marginali, per modelli mistura PY. A differenza di altri algoritmi condizionali, l’efficienza numerica dell’ICS è robusta rispetto alla specificazione dei parametri del PY. Gli step per implementare l’ICS sono descritti in dettaglio e le performance sono comparate con gli algoritmi più popolari. Infine l’ICS viene usato per definire un nuovo algoritmo efficiente per la classe di modelli mistura GM-dipendenti DP (Lijoi et al., 2014a; Lijoi et al., 2014b), per dati parzialmente scambiabili. Nel quarto capitolo studiamo alcune proprietà delle Gibbs-type priors. Il risultato principale riguarda un campione scambiabile estratto da una Gibbs-type prior e propone una rappresentazione conveniente della distribuzione della dimensione del cluster per l’osservazione (m+1)esima, dato un campione non osservato di ampiezza m. Dallo studio di questa distribuzione deriviamo una strategia, semplice ed utile, per elicitare i parametri di una Gibbs-type prior, nel contesto dei modelli mistura con una misura misturante Gibbs-type. I risultati negli ultimi tre capitoli sono supportati da esaustivi studi di simulazioni ed illustrazioni in ambito atronomico.<br>Bayesian nonparametric mixtures are flexible models for density estimation and clustering, nowadays a standard tool in the toolbox of applied statisticians. The first proposal of such models was the Dirichlet process (DP) (Ferguson, 1973) mixture of Gaussian kernels by Lo (1984), contribution which paved the way to the definition of a wide variety of nonparametric mixture models. In recent years, increasing interest has been dedicated to the definition of mixture models based on nonparametric mixing measures that go beyond the DP. Among these measures, the Pitman-Yor process (PY) (Perman et al., 1992; Pitman, 1995) and, more in general, the class of Gibbs-type priors (see e.g. De Blasi et al., 2015) stand out for conveniently combining mathematical tractability, interpretability and modelling flexibility. In this thesis we investigate three aspects of nonparametric mixture models, which, in turn, concern their modelling, computational and distributional properties. The thesis is organized as follows. The first chapter proposes a coincise review of the area of Bayesian nonparametric statistics, with focus on tools and models that will be considered in the following chapters. We first introduce the notions of exchangeability, exchangeable partitions and discrete random probability measures. We then focus on the DP and the PY case, main ingredients of second and third chapter, respectively. Finally, we briefly discuss the rationale behind the definition of more general classes of discrete nonparametric priors. In the second chapter we propose a thorough study on the effect of invertible affine transformations of the data on the posterior distribution of DP mixture models, with particular attention to DP mixtures of Gaussian kernels (DPM-G). First, we provide an explicit result relating model parameters and transformations of the data. Second, we formalize the notion of asymptotic robustness of a model under affine transformations of the data and prove an asymptotic result which, by relying on the asymptotic consistency of DPM-G models, show that, under mild assumptions on the data-generating distribution, DPM-G are asymptotically robust. The third chapter presents the ICS, a novel conditional sampling scheme for PY mixture models, based on a useful representation of the posterior distribution of a PY (Pitman, 1996) and on an importance sampling idea, similar in spirit to the augmentation step of the celebrated Algorithm 8 of Neal (2000). The proposed method conveniently combines the best features of state-of-the-art conditional and marginal methods for PY mixture models. Importantly, and unlike its most popular conditional competitors, the numerical efficiency of the ICS is robust to the specification of the parameters of the PY. The steps for implementing the ICS are described in detail and its performance is compared with that one of popular competing algorithms. Finally, the ICS is used as a building block for devising a new efficient algorithm for the class of GM-dependent DP mixture models (Lijoi et al., 2014a; Lijoi et al., 2014b), for partially exchangeable data. In the fourth chapter we study some distributional properties Gibbs-type priors. The main result focuses on an exchangeable sample from a Gibbs-type prior and provides a conveniently simple description of the distribution of the size of the cluster the ( m + 1 ) th observation is assigned to, given an unobserved sample of size m. The study of such distribution provides the tools for a simple, yet useful, strategy for prior elicitation of the parameters of a Gibbs-type prior, in the context of Gibbs-type mixture models. The results in the last three chapters are supported by exhaustive simulation studies and illustrated by analysing astronomical datasets.
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Campbell, Trevor D. J. (Trevor David Jan). "Truncated Bayesian nonparametrics." Thesis, Massachusetts Institute of Technology, 2016. http://hdl.handle.net/1721.1/107047.

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Thesis: Ph. D., Massachusetts Institute of Technology, Department of Aeronautics and Astronautics, 2016.<br>Cataloged from PDF version of thesis.<br>Includes bibliographical references (pages 167-175).<br>Many datasets can be thought of as expressing a collection of underlying traits with unknown cardinality. Moreover, these datasets are often persistently growing, and we expect the number of expressed traits to likewise increase over time. Priors from Bayesian nonparametrics are well-suited to this modeling challenge: they generate a countably infinite number of underlying traits, which allows the number of expressed traits to both be random and to grow with the dataset size. We also require corresponding streaming, distributed inference algorithms that handle persistently growing datasets without slowing down over time. However, a key ingredient in streaming, distributed inference-an explicit representation of the latent variables used to statistically decouple the data-is not available for nonparametric priors, as we cannot simulate or store infinitely many random variables in practice. One approach is to approximate the nonparametric prior by developing a sequential representation-such that the traits are generated by a sequence of finite-dimensional distributions-and subsequently truncating it at some finite level, thus allowing explicit representation. However, truncated sequential representations have been developed only for a small number of priors in Bayesian nonparametrics, and the order they impose on the traits creates identifiability issues in the streaming, distributed setting. This thesis provides a comprehensive theoretical treatment of sequential representations and truncation in Bayesian nonparametrics. It details three sequential representations of a large class of nonparametric priors, and analyzes their truncation error and computational complexity. The results generalize and improve upon those existing in the literature. Next, the truncated explicit representations are used to develop the first streaming, distributed, asynchronous inference procedures for models from Bayesian nonparametrics. The combinatorial issues associated with trait identifiability in such models are resolved via a novel matching optimization. The resulting algorithms are fast, learning rate-free, and truncation-free. Taken together, these contributions provide the practitioner with the means to (1) develop multiple finite approximations for a given nonparametric prior; (2) determine which is the best for their application; and (3) use that approximation in the development of efficient streaming, distributed, asynchronous inference algorithms.<br>by Trevor David Jan Campbell.<br>Ph. D.
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Li, Jiexiang. "Nonparametric spatial estimation." [Bloomington, Ind.] : Indiana University, 2006. http://gateway.proquest.com/openurl?url_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation&res_dat=xri:pqdiss&rft_dat=xri:pqdiss:3223036.

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Thesis (Ph.D.)--Indiana University, Dept. of Mathematics, 2006.<br>"Title from dissertation home page (viewed June 28, 2007)." Source: Dissertation Abstracts International, Volume: 67-06, Section: B, page: 3167. Adviser: Lanh Tat Tran.
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Straub, Julian Ph D. Massachusetts Institute of Technology. "Nonparametric directional perception." Thesis, Massachusetts Institute of Technology, 2017. http://hdl.handle.net/1721.1/112029.

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Thesis: Ph. D., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2017.<br>Cataloged from PDF version of thesis.<br>Includes bibliographical references (pages 239-257).<br>Artificial perception systems, like autonomous cars and augmented reality headsets, rely on dense 3D sensing technology such as RGB-D cameras and LiDAR. scanners. Due to the structural simplicity of man-made environments, understanding and leveraging not only the 3D data but also the local orientations of the constituent surfaces, has huge potential. From an indoor scene to large-scale urban environments, a large fraction of the surfaces can be described by just a few planes with even fewer different normal directions. This sparsity is evident in the surface normal distributions, which exhibit a small number of concentrated clusters. In this work, I draw a rigorous connection between surface normal distributions and 3D structure, and explore this connection in light of different environmental assumptions to further 3D perception. Specifically, I propose the concepts of the Manhattan Frame and the unconstrained directional segmentation. These capture, in the space of surface normals, scenes composed of multiple Manhattan Worlds and more general Stata Center Worlds, in which the orthogonality assumption of the Manhattan World is not applicable. This exploration is theoretically founded in Bayesian nonparametric models, which capture two key properties of the 3D sensing process of an artificial perception system: (1) the inherent sequential nature of data acquisition and (2) that the required model complexity grows with the amount of observed data. Herein, I derive inference algorithms for directional clustering and segmentation which inherently exploit and respect these properties. The fundamental insights gleaned from the connection between surface normal distributions and 3D structure lead to practical advances in scene segmentation, drift-free rotation estimation, global point cloud registration and real-time direction-aware 3D reconstruction to aid artificial perception systems.<br>by Julian Straub.<br>Ph. D.
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Xu, Tianbing. "Nonparametric evolutionary clustering." Diss., Online access via UMI:, 2009.

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Rangel, Ruiz Ricardo. "Nonparametric and semi-nonparametric approaches to the demand for liquid assets." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 2001. http://www.collectionscanada.ca/obj/s4/f2/dsk3/ftp04/MQ64924.pdf.

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Yuan, Lin. "Bayesian nonparametric survival analysis." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1997. http://www.collectionscanada.ca/obj/s4/f2/dsk3/ftp04/nq22253.pdf.

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Bush, Helen Meyers. "Nonparametric multivariate quality control." Diss., Georgia Institute of Technology, 1996. http://hdl.handle.net/1853/25571.

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Pedroso, Estevam de Souza Camila. "Switching nonparametric regression models." Thesis, University of British Columbia, 2013. http://hdl.handle.net/2429/45130.

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In this thesis, we propose a methodology to analyze data arising from a curve that, over its domain, switches among J states. We consider a sequence of response variables, where each response y depends on a covariate x according to an unobserved state z, also called a hidden or latent state. The states form a stochastic process and their possible values are j=1,...,J. If z equals j the expected response of y is one of J unknown smooth functions evaluated at x. We call this model a switching nonparametric regression model. In a Bayesian switching nonparametric regression model the uncertainty about the functions is formulated by modeling the functions as realizations of stochastic processes. In a frequentist switching nonparametric regression model the functions are merely assumed to be smooth. We consider two different data structures: one with N replicates and the other with one single realization. For the hidden states, we consider those that are independent and identically distributed and those that follow a Markov structure. We develop an EM algorithm to estimate the parameters of the latent state process and the functions corresponding to the J states. Standard errors for the parameter estimates of the state process are also obtained. We investigate the frequentist properties of the proposed estimates via simulation studies. Two different applications of the proposed methodology are presented. In the first application we analyze the well-known motorcycle data in an innovative way: treating the data as coming from J>1 simulated accident runs with unobserved run labels. In the second application we analyze daytime power usage on business days in a building treating each day as a replicate and modeling power usage as arising from two functions, one function giving power usage when the cooling system of the building is off, the other function giving power usage when the cooling system is on.
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Gosling, John Paul. "Elicitation : a nonparametric view." Thesis, University of Sheffield, 2005. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.425613.

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Books on the topic "Nonparametrica"

1

Hjort, Nils Lid, Chris Holmes, Peter Muller, and Stephen G. Walker, eds. Bayesian Nonparametrics. Cambridge University Press, 2009. http://dx.doi.org/10.1017/cbo9780511802478.

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Bayesian nonparametrics. Cambridge University Press, 2010.

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Lid, Hjort Nils, ed. Bayesian nonparametrics. Cambridge University Press, 2009.

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Gibbons, Jean. Nonparametric Statistics. SAGE Publications, Inc., 1993. http://dx.doi.org/10.4135/9781412985314.

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Bertail, Patrice, Delphine Blanke, Pierre-André Cornillon, and Eric Matzner-Løber, eds. Nonparametric Statistics. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-96941-1.

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Cao, Ricardo, Wenceslao González Manteiga, and Juan Romo, eds. Nonparametric Statistics. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-41582-6.

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La Rocca, Michele, Brunero Liseo, and Luigi Salmaso, eds. Nonparametric Statistics. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-57306-5.

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Klemelä, Jussi. Nonparametric Finance. John Wiley & Sons, Inc., 2018. http://dx.doi.org/10.1002/9781119409137.

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Aman, Ullah, ed. Nonparametric econometrics. Cambridge University Press, 1999.

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Pagan, A. R. Nonparametric Econometrics. Cambridge University Press, 1999.

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Book chapters on the topic "Nonparametrica"

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Heiberger, Richard M., and Burt Holland. "Nonparametrics." In Statistical Analysis and Data Display. Springer New York, 2015. http://dx.doi.org/10.1007/978-1-4939-2122-5_16.

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Heiberger, Richard M., and Burt Holland. "Nonparametrics." In Statistical Analysis and Data Display. Springer New York, 2004. http://dx.doi.org/10.1007/978-1-4757-4284-8_16.

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Chow, Shein-Chung, Jun Shao, Hansheng Wang, and Yuliya Lokhnygina. "Nonparametrics." In Sample Size Calculations in Clinical Research: Third Edition. Chapman and Hall/CRC, 2017. http://dx.doi.org/10.1201/9781315183084-14.

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Lee, Myoung-jae. "Semi-Nonparametrics." In Methods of Moments and Semiparametric Econometrics for Limited Dependent Variable Models. Springer New York, 1996. http://dx.doi.org/10.1007/978-1-4757-2550-6_10.

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Walker, Stephen Graham. "Bayesian Nonparametrics." In The New Palgrave Dictionary of Economics. Palgrave Macmillan UK, 2008. http://dx.doi.org/10.1057/978-1-349-95121-5_2596-1.

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Clarke, Bertrand, Ernest Fokoué, and Hao Helen Zhang. "Alternative Nonparametrics." In Principles and Theory for Data Mining and Machine Learning. Springer New York, 2009. http://dx.doi.org/10.1007/978-0-387-98135-2_6.

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Sun, Jianguo, and Xingqiu Zhao. "Nonparametric Estimation." In Statistics for Biology and Health. Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4614-8715-9_3.

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Fahrmeir, Ludwig, Thomas Kneib, Stefan Lang, and Brian Marx. "Nonparametric Regression." In Regression. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-34333-9_8.

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Kutoyants, Yu A. "Nonparametric Estimation." In Statistical Inference for Spatial Poisson Processes. Springer New York, 1998. http://dx.doi.org/10.1007/978-1-4612-1706-0_7.

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Koh, Eunsook T., and Willis L. Owen. "Nonparametric Statistics." In Introduction to Nutrition and Health Research. Springer US, 2000. http://dx.doi.org/10.1007/978-1-4615-1401-5_9.

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Conference papers on the topic "Nonparametrica"

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Tsang, Coates, and Nowak. "Nonparametric Internet tomography." In IEEE International Conference on Acoustics Speech and Signal Processing ICASSP-02. IEEE, 2002. http://dx.doi.org/10.1109/icassp.2002.1006175.

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Tsang, Yolanda, Mark Coates, and Robert Nowak. "Nonparametric internet tomography." In Proceedings of ICASSP '02. IEEE, 2002. http://dx.doi.org/10.1109/icassp.2002.5745035.

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Mattar, Marwan A., Michael G. Ross, and Erik G. Learned-Miller. "Nonparametric curve alignment." In ICASSP 2009 - 2009 IEEE International Conference on Acoustics, Speech and Signal Processing. IEEE, 2009. http://dx.doi.org/10.1109/icassp.2009.4960369.

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PARK, BYEONG U. "NONPARAMETRIC ADDITIVE REGRESSION." In International Congress of Mathematicians 2018. WORLD SCIENTIFIC, 2019. http://dx.doi.org/10.1142/9789813272880_0169.

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Duan, Ling-Yu, Min Xu, Qi Tian, and Chang-Sheng Xu. "Nonparametric motion model." In the 12th annual ACM international conference. ACM Press, 2004. http://dx.doi.org/10.1145/1027527.1027700.

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Yazhou Liu, Hongxun Yao, Wen Gao, Xilin Chen, and Debin Zhao. "Nonparametric Background Generation." In 18th International Conference on Pattern Recognition (ICPR'06). IEEE, 2006. http://dx.doi.org/10.1109/icpr.2006.868.

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Enright, M. P., R. C. McClung, S. J. Hudak, and H. R. Millwater. "Application of Nonparametric Methods to Rainflow Stress Density Estimation of Gas Turbine Engine Usage." In ASME Turbo Expo 2006: Power for Land, Sea, and Air. ASMEDC, 2006. http://dx.doi.org/10.1115/gt2006-90780.

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The risk of fracture associated with high energy rotating components in aircraft gas turbine engines can be sensitive to small changes in applied stress values which are often difficult to measure and predict. Although a parametric approach is often used to characterize random variables, it is difficult to apply to multimodal densities. Nonparametric methods provide a direct fit to the data, and can be used to estimate the multimodal densities often associated with rainflow stress data. In this paper, a comparison of parametric and nonparametric methods is presented for density estimation of rainflow stress profiles associated with military aircraft gas turbine engine usages. A nonparametric adaptive kernel density estimator algorithm is illustrated for standard parametric probability density functions and for rainflow stress pairs associated with F-16/F100 engine usages. The kernel estimates are compared to parametric estimates, including a hybrid approach based on separate treatment of maximum stress pairs. The results provide some insight regarding the strengths and weaknesses of parametric and nonparametric density estimation methods for gas turbine engines, and can be used to develop improved stress estimates for probabilistic life predictions.
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Hague, Stephen, and Simaan AbouRizk. "Nonparametric frequency polygon estimation for modeling input data." In The 19th International Conference on Modelling and Applied Simulation. CAL-TEK srl, 2019. http://dx.doi.org/10.46354/i3m.2019.mas.020.

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To construct valid probability distributions solely from input data, this paper compares three nonparametric density estimators: (1) histograms, (2) Kernel Density Estimation, and (3) Frequency Polygon Estimation. A pseudocode is implemented, a practical example is illustrated, and the Simphony.NET simulation environment is used to fit the nonparametric frequency polygon to a set of data to recreate it as a posterior distribution via the Metropolis-Hastings algorithm.
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Capiez-Lernout, Evange´line, and Christian Soize. "Specifying Manufacturing Tolerances for a Given Amplification Factor: A Nonparametric Probabilistic Methodology." In ASME Turbo Expo 2003, collocated with the 2003 International Joint Power Generation Conference. ASMEDC, 2003. http://dx.doi.org/10.1115/gt2003-38050.

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It is known that the forced response of mistuned bladed disks can strongly be amplified in comparison with the forced response of the tuned system. The random character of mistuning thus requires the construction of probabilistics models of random uncertainties. This paper presents a nonparametric probabilistic model of random uncertainties which is adapted to the problematics of the blade mistuning. This nonparametric approach allows all the uncertainties yielding mistuning (manufacturing tolerances, dispersion of materials) to be taken into account and includes also the uncertainties due to the modeling errors. This new probabilistic model takes into account both the mistuning of the blade eigenfrequencies and the blade modal shapes. The first point concerns the construction of this nonparametric approach in order to perform a mistuning analysis. The second part is devoted to the inverse problem associated with the manufacturing tolerances. A relationship between the manufacturing tolerances and the level of mistuning is also constructed.
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Hines, J. Wesley, and Dustin R. Garvey. "Nonparametric model-based prognostics." In 2008 Annual Reliability and Maintainability Symposium. IEEE, 2008. http://dx.doi.org/10.1109/rams.2008.4925841.

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Reports on the topic "Nonparametrica"

1

Owen, Arthur B. Nonparametric Conditional Estimation. Office of Scientific and Technical Information (OSTI), 2018. http://dx.doi.org/10.2172/1454025.

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Owen, Arthur B. Nonparametric Conditional Estimation. Defense Technical Information Center, 1987. http://dx.doi.org/10.21236/ada590998.

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Horowitz, Joel L. Nonparametric additive models. Institute for Fiscal Studies, 2012. http://dx.doi.org/10.1920/wp.cem.2012.2012.

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Kim, Dongwoo, Denis Chetverikov, and Daniel Wilhelm. Nonparametric instrumental variable estimation. The IFS, 2017. http://dx.doi.org/10.1920/wp.cem.2017.4717.

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Owen, Art. Nonparametric Likelihood Ratio Intervals. Defense Technical Information Center, 1985. http://dx.doi.org/10.21236/ada162978.

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Dew-Becker, Ian, and Charles Nathanson. Directed Attention and Nonparametric Learning. National Bureau of Economic Research, 2017. http://dx.doi.org/10.3386/w23917.

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Chamberlain, Gary, and Guido Imbens. Nonparametric Applications of Bayesian Inference. National Bureau of Economic Research, 1996. http://dx.doi.org/10.3386/t0200.

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Andersen, Torben, Tim Bollerslev, and Francis Diebold. Parametric and Nonparametric Volatility Measurement. National Bureau of Economic Research, 2002. http://dx.doi.org/10.3386/t0279.

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Block, Henry W., and Thomas H. Savits. Multivariate Nonparametric Classes in Reliability. Defense Technical Information Center, 1985. http://dx.doi.org/10.21236/ada185645.

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Wu, Jin Chu, and Charles L. Wilson. Nonparametric analysis of fingerprint data. National Institute of Standards and Technology, 2005. http://dx.doi.org/10.6028/nist.ir.7226.

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