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Abstrak :
ABSTRAK
Penaksiran parameter dalam model regresi memiliki dua pendekatan yaitu pendekatan regresi parametrik dan pendekatan regresi nonparametrik. Dalam regresi parametrik bentuk dari kurva hubungan antara variabel respon dan variabel prediktor sudah ditentukan berdasarkan plot data, sedangkan dalam regresi nonparametrik bentuk dari kurva tidak diketahui. Salah satu regresi nonparametrik yang dapat digunakan adalah regresi spline. Regresi spline adalah suatu piecewise polynomial yang dihubungkan oleh titik-titik bersama yang disebut dengan knot. Regresi spline yang menggunakan fungsi basis B Spline disebut dengan regresi B Spline. Pada umumnya estimasi parameter regresi B Spline dilakukan dengan menggunakan metode OLS Ordinary Least Square. Namun, dengan metode OLS akan menyebabkan plot taksiran kurva regresi menjadi fluktuatif apabila pemilihan jumlah knot terlalu banyak. Untuk itu diperlukan suatu tambahan kendala berupa penalty yang didalamnya mengandung smoothing parameter sehingga diperoleh taksiran ideal. Metode estimasi parameter ini dikenal dengan metode PLS Penalized Least Square . Metode PLS dengan penalty yang merupakan integral kuadrat derivatif kedua dari taksiran kurva disebut juga dengan metode o rsquo;sullivan penalized spline. Pada penerapan contoh data, didapat 23 buah knot dan smoothing parameter sebesar 0.68.

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ABSTRACT
Parameter estimation of regression model has two approaches, that is parametric and nonparametric regression approach. In parametric regression, the shape of regression curve is determined based on scatterplot of dependent variable vs independent variable, whereas in the nonparametric regression, the shape of the curve is unknown. One of the nonparametric regression is spline regression. Spline regression is piecewise polynomials that connected by the knots. Spline regression using B Spline basis function is B Spline regression. In B spline regression, parameter estimation were fitted by OLS Ordinary Least Square method. However, the OLS method will lead the plot of estimated regression curve be fluctuative when using too much knots. Therefore, it needs additional constraint of penalty that contain smoothing parameter to obtain ideal fit result. This parameter estimation method known as PLS Penalized Least Square method. The estimate PLS method used penalty which is the integral of the square of second derivative of the estimate curve that called o 39 sullivan penalized spline method. In the application of sample data, 23 is used knots and the smoothing parameters is 0.68.

Abstrak :
This book serves well as an introduction into the more theoretical aspects of the use of spline models. It develops a theory and practice for the estimation of functions from noisy data on functionals. The simplest example is the estimation of a smooth curve, given noisy observations on a finite number of its values. The estimate is a polynomial smoothing spline. By placing this smoothing problem in the setting of reproducing kernel Hilbert spaces, a theory is developed which includes univariate smoothing splines, thin plate splines in d dimensions, splines on the sphere, additive splines, and interaction splines in a single framework. A straightforward generalization allows the theory to encompass the very important area of (Tikhonov) regularization methods for ill-posed inverse problems. Convergence properties, data based smoothing parameter selection, confidence intervals, and numerical methods are established which are appropriate to a wide variety of problems which fall within this framework. Methods for including side conditions and other prior information in solving ill-posed inverse problems are included. Data which involves samples of random variables with Gaussian, Poisson, binomial, and other distributions are treated in a unified optimization context. Experimental design questions, i.e., which functionals should be observed, are studied in a general context. Extensions to distributed parameter system identification problems are made by considering implicitly defined functionals.

Abstrak :
Dibahas visualisasi kurva hampiran dengan menggunakan metode Hampiran B-Spline. Dalam hampiran ini diberikan sejumlah data koordinat, dengan menggunakan kombinasi linear dari sejumlah basis B-Spline akan diperoleh kurva hampiran yang dimaksud. Fungsi basis yang digunakan disini berderajat 1,2, dan 3. Kurva yang dihasilkan kemudian akan divisualisasikan pada jendela gratis. Jendela grafis dibuat pada aplikasi yang dijalankan dengan sistem Microsoft Windows 16 bit dan 32 bit. Platform yang dipakai untuk membuat aplikasi adalah Borland C++ ver. 4.5 for Windows, pembuatan kelas baru yang merupakan turunan dari objek-objek yang telah ada pada platform ini sangat menunjang pada aplikasi yang dibentuk. Dengan sub selang penggambaran h diberikan oleh pengguna, maka semakin kecil h kurva hampiran yang dibentuk akan semakin halus. Semakin tinggi derajat fungsi basis yang digunakan maka kurva yang dihasilkan akan semakin halus, akan tetapi jumlah operasi rekursif yang dilakukan semakin banyak.

Abstrak :
Michael A. Matt constructs two trivariate local Lagrange interpolation methods which yield optimal approximation order and Cr macro-elements based on the Alfeld and the Worsey-Farin split of a tetrahedral partition. The first interpolation method is based on cubic C1 splines over type-4 cube partitions, for which numerical tests are given. The second is the first trivariate Lagrange interpolation method using C2 splines. It is based on arbitrary tetrahedral partitions using splines of degree nine. The author constructs trivariate macro-elements based on the Alfeld split, where each tetrahedron is divided into four subtetrahedra, and the Worsey-Farin split, where each tetrahedron is divided into twelve subtetrahedra, of a tetrahedral partition. In order to obtain the macro-elements based on the Worsey-Farin split minimal determining sets for Cr macro-elements are constructed over the Clough-Tocher split of a triangle, which are more variable than those in the literature.

Abstrak :
As this monograph shows, the purpose of cardinal spline interpolation is to bridge the gap between the linear spline and the cardinal series. The author explains cardinal spline functions, the basic properties of B-splines, including B- splines with equidistant knots and cardinal splines represented in terms of B-splines, and exponential Euler splines, leading to the most important case and central problem of the book-- cardinal spline interpolation, with main results, proofs, and some applications. Other topics discussed include cardinal Hermite interpolation, semi-cardinal interpolation, finite spline interpolation problems, extremum and limit properties, equidistant spline interpolation applied to approximations of Fourier transforms, and the smoothing of histograms.

Abstrak :
Estimation of regression curve usually conducted using three methods; parametric method, non- parametric method, and semi-parametric methods...

Abstrak :
The subject of multivariate splines has become a rapidly growing field of mathematical research. The author presents the subject from an elementary point of view that parallels the theory and development of univariate spline analysis. To compensate for the missing proofs and details, an extensive bibliography has been included. There is a presentation of open problems with an emphasis on the theory and applications to computer-aided design, data analysis, and surface fitting. Applied mathematicians and engineers working in the areas of curve fitting, finite element methods, computer-aided geometric design, signal processing, mathematical modelling, computer-aided design, computer-aided manufacturing, and circuits and systems will find this monograph essential to their research.

Abstrak :
Dalam tesis ini disusun representasi multiresolusi berbasis wavelets untuk kurva B-spline kubik yang menginterpolasi titik-titik ujung yang mendukung beberapa tipe pengeditan kurva, yaitu penghalusan kurva dengan tingkat resolusi kontinyu untuk menghilangkan detail-detail kurva yang tidak diinginkan, pengeditan bentuk keseluruhan kurva dengan tetap mempertahankan detail-detailnya, perubahan detail-detail kurva tanpa mempengaruhi bentuk keseluruhannya, dan pengeditan satu bagian tertentu dari kurva melalui manipulasi secara langsung terhadap titik-titik kontrolnya. Untuk menguji kemampuan representasi multiresolusi dalam mendukung empat tipe manipulasi kurva tersebut, disusun program pengeditan kurva dengan menggunakan bahasa pemrograman Visual C++ pada komputer Pentium 133 MHz, memori 16 Mbyte, sistem operasi Windows 95, lingkungan pengembangan Microsoft Development Studio 97 dan pustaka Microsoft Foundation Class. Dari hasil uji coba program diketahui bahwa representasi multiresolusi memberikan dukungan yang sangat baik terhadap tipe-tipe pengeditan seperti yang disebutkan di atas. Representasi multiresolusi tidak membutuhkan memori penyimpan ekstra selain dari yang digunakan untuk menyimpan titik kontrol. Dui basil uji coba program menggunakan ratusan titik kontrol, algoritma beijalan cuki p cepat dan mernadai berkaitan dengan tuntutan komunikasi interaktif antara user dan program.