- TESTING FOR MONOTONICITY OF A REGRESSION MEAN WITHOUT SELECTING A BANDWIDTH 1
- Comparing the shapes of regression functions
- Distributions This Appendix contains the histograms of the distributions of MSE and log(MSE)
- Bibliography Altman, N.S. (1990), ``Kernel Smoothing of Data with Correlated Errors'', J.
- Bumps: what are they? In this chapter we outline the framework of our problem. The next section presents
- Penalized Regression with ModelBased Nancy E. Heckman and J. O. Ramsay
- Monthly Data 0 12 24 36 48 60 72
- Smoothing Parameter Selection When Errors are Correlated and Application to Ozone Data
- Simulation Results for Bivariate Data with 50% Censoring
- Application to the Diabetic Retinopathy Study
- Introduction Bivariate failure time data arise when study subject units are paired. Examples of paired
- Simulations The main goal of this thesis is to estimate a regression function from data of
- Line Transects of Two Dimensional Random Fields: Estimation and Design
- Methods of Estimation In this chapter, we give a brief summary of the nonparametric techniques used in
- Hazard Regression In this chapter we define a parametric regression model for log hazard functions of
- Bibliography [1] Abrahamowicz, M. Ciampi, A. and Ramsay, J. O. (1992). Nonparametric Density
- Regression Space In the previous chapter we discussed the regression model for log hazard functions for
- Nonparametric testing for a monotone hazard function via normalized spacings
- CriSP a Tool for Bump Hunting Jaroslaw Harezlak
- The Canadian Journal of Statistics Vol. 28, No. ?, 2000, Pages ??????
- Penalized Regression, Mixed Effects Models and Appropriate Modelling
- ESTIMATING AND DEPICTING THE STRUCTURE OF A DISTRIBUTION OF RANDOM FUNCTIONS
- Bibliography [1] Babaud, J., Witkin, A.P., Baudin, M., and Duda, R.O. (1986), ``Uniqueness of
- In order to examine the proposed testing procedure for the number of bumps, we conducted a simulation study. We used several functions to assess the performance
- Some Theory for LSpline Smoothing J. O. Ramsay
- This chapter contains discussion of a simulation study of estimates in the log hazard regression model. Our main aim is to check the bias and variability of our estimates for
- the university of british columbia department of statistics
- Introduction Estimating the underlying signal, often called the regression function, of a set
- Conclusions In this thesis, the new, smoothing parameter based test CriSP for the number of
- BUMP HUNTING IN REGRESSION Jaroslaw Harezlak
- Overview of Existing Methods. This chapter contains a brief summary of the key methods available for uni
- University of British Columbia Department of Statistics
- Functional data analysis in evolutionary biology Nancy E. Heckmana
- Conclusion and Discussion The motivation of this work comes from an environmental problem. Indeed,
- Introduction Statisticians often want to find an underlying ``true'' regression function when given
- Application to Air Pollution 5.1 Introduction
- In this thesis a flexible parametric model, log hazard regression model for paired censored failure times, is proposed. In this model Bsplines are used to estimate the log hazards
- Male results In Table A.1 the results of the bootstrap procedure are summarized. We show the
- Testing for Bumps As mentioned in section 2.3, we have to look separately at the density estimation and
- Female results In Table B.1 the results of the bootstrap procedure are summarized. We show the
- LOG HAZARD REGRESSION Huiying Sun
- Local Linear Forecasting Xiaochun Li
- Smoothing Parameter Selection When the Errors are Correlated
