00849nam2-2200301---450-99000877968040332120081215124905.0000877968FED01000877968(Aleph)000877968FED0100087796820081215d1991----km-y0itay50------baitaITy-------001yyCremonaIstat, Istituto nazionale di statisticaRomaIstat1991187, [14] p.28 cm0010003470822001Caratteristiche strutturali delle aziende agricoleLombardiaAgricolturaIstat374421ITUNINARICAUNIMARCBK990008779680403321Istat Cens.A. 1991 3(19)I.G. 931ILFGEILFGECremona303792UNINA05450nam 2200661Ia 450 991046180920332120200520144314.01-283-63596-8981-4405-49-3(CKB)2670000000272698(EBL)1044403(OCoLC)811820296(SSID)ssj0000914681(PQKBManifestationID)11570774(PQKBTitleCode)TC0000914681(PQKBWorkID)10862072(PQKB)11144507(MiAaPQ)EBC1044403(WSP)00002798(Au-PeEL)EBL1044403(CaPaEBR)ebr10607779(CaONFJC)MIL394842(EXLCZ)99267000000027269819941011d2012 uy 0engur|n|---|||||txtccrKernel smoothing in MATLAB[electronic resource] theory and practice of kernel smoothing /Ivanka Horová, Jan Koláček, Jiří ZelinkaSingapore ;Hackensack, NJ World Scientific20121 online resource (242 p.)Description based upon print version of record.981-4405-48-5 Includes bibliographical references (p. 213-223) and index.Preface; Contents; 1. Introduction; 1.1 Kernels and their properties; 1.2 Use of MATLAB toolbox; 1.3 Complements; 2. Univariate kernel density estimation; 2.1 Basic definition; 2.2 Statistical properties of the estimate; 2.3 Choosing the shape of the kernel; 2.4 Choosing the bandwidth; 2.4.1 Reference rule; 2.4.2 Maximal smoothing principle; 2.4.3 Cross-validation methods; 2.4.4 Plug-in method; 2.4.5 Iterative method; 2.5 Density derivative estimation; 2.5.1 Choosing the bandwidth; 2.6 Automatic procedure for simultaneous choice of the kernel, the bandwidth and the kernel order2.7 Boundary effects2.7.1 Generalized reflection method; 2.8 Simulations; 2.9 Application to real data; 2.9.1 Buffalo snowfall data; 2.9.2 Concentration of cholesterol; 2.10 Use of MATLAB toolbox; 2.10.1 Running the program; 2.10.2 Main figure; 2.10.3 Setting the parameters; 2.10.4 Eye-control method; 2.10.5 The final estimation; 2.11 Complements; 3. Kernel estimation of a distribution function; 3.1 Basic definition; 3.2 Statistical properties of the estimate; 3.3 Choosing the bandwidth; 3.3.1 Cross-validation methods; 3.3.2 Maximal smoothing principle; 3.3.3 Plug-in methods3.3.4 Iterative method3.4 Boundary effects; 3.4.1 Generalized reflection method; 3.5 Application to data; 3.6 Simulations; 3.7 Application to real data; 3.7.1 Trout PCB data; 3.8 Use of MATLAB toolbox; 3.8.1 Running the program; 3.8.2 Main figure; 3.8.3 Setting the parameters; 3.8.4 Eye-control method; 3.8.5 The final estimation; 3.9 Complements; 4. Kernel estimation and reliability assessment; 4.1 Basic Definition; 4.2 Estimation of ROC curves; 4.2.1 Binormal model; 4.2.2 Nonparametric estimates; 4.3 Summary indices based on the ROC curve; 4.3.1 Area under the ROC curve4.3.2 Maximum improvement of sensitivity over chance diagonal (MIS)4.4 Other indices of reliability assessment; 4.4.1 Cumulative Lift; 4.4.2 Lift Ratio; 4.4.3 Integrated Relative Lift; 4.4.4 Information Value; 4.4.5 KR index; 4.5 Application to real data; 4.5.1 Head trauma data; 4.5.2 Pancreatic cancer data; 4.5.3 Consumer loans data; 4.6 Use of MATLAB toolbox; 4.6.1 Running the program; 4.6.2 Start menu; 4.6.3 Simulation menu; 4.6.4 The final estimation; 5. Kernel estimation of a hazard function; 5.1 Basic definition; 5.2 Statistical properties of the estimate; 5.3 Choosing the bandwidth5.3.1 Cross-validation method5.3.2 Maximum likelihood method; 5.3.3 Iterative method; 5.3.4 Acceptable bandwidths; 5.3.5 Points of the most rapid change; 5.4 Description of algorithm; 5.5 Application to real data; 5.5.1 Breast carcinoma data; 5.5.2 Cervix carcinoma data; 5.5.3 Chronic lymphocytic leukaemia; 5.5.4 Bone marrow transplant; 5.6 Use of MATLAB toolbox; 5.6.1 Running the program; 5.6.2 Main figure; 5.6.3 Setting the parameters; 5.6.4 Eye-control method; 5.6.5 The final estimation; 5.7 Complements; Simulation of lifetimes; Simulation of censoring times6. Kernel estimation of a regression functionMethods of kernel estimates represent one of the most effective nonparametric smoothing techniques. These methods are simple to understand and they possess very good statistical properties. This book provides a concise and comprehensive overview of statistical theory and in addition, emphasis is given to the implementation of presented methods in Matlab. All created programs are included in a special toolbox which is an integral part of the book. This toolbox contains many Matlab scripts useful for kernel smoothing of density, cumulative distribution function, regression function, hazard functSmoothing (Statistics)Kernel functionsElectronic books.Smoothing (Statistics)Kernel functions.519.5Horová Ivanka862494Koláček Jan862495Zelinka Jiří862496MiAaPQMiAaPQMiAaPQBOOK9910461809203321Kernel smoothing in MATLAB1925234UNINA