Access

You are not currently logged in.

Access your personal account or get JSTOR access through your library or other institution:

login

Log in to your personal account or through your institution.

If You Use a Screen Reader

This content is available through Read Online (Free) program, which relies on page scans. Since scans are not currently available to screen readers, please contact JSTOR User Support for access. We'll provide a PDF copy for your screen reader.

Some Aspects of the Spline Smoothing Approach to Non-Parametric Regression Curve Fitting

B. W. Silverman
Journal of the Royal Statistical Society. Series B (Methodological)
Vol. 47, No. 1 (1985), pp. 1-52
Published by: Wiley for the Royal Statistical Society
Stable URL: http://www.jstor.org/stable/2345542
Page Count: 52
  • Read Online (Free)
  • Download ($29.00)
  • Subscribe ($19.50)
  • Cite this Item
Since scans are not currently available to screen readers, please contact JSTOR User Support for access. We'll provide a PDF copy for your screen reader.
Some Aspects of the Spline Smoothing Approach to Non-Parametric Regression Curve Fitting
Preview not available

Abstract

Non-parametric regression using cubic splines is an attractive, flexible and widely-applicable approach to curve estimation. Although the basic idea was formulated many years ago, the method is not as widely known or adopted as perhaps it should be. The topics and examples discussed in this paper are intended to promote the understanding and extend the practicability of the spline smoothing methodology. Particular subjects covered include the basic principles of the method; the relation with moving average and other smoothing methods; the automatic choice of the amount of smoothing; and the use of residuals for diagnostic checking and model adaptation. The question of providing inference regions for curves-and for relevant properties of curves--is approached via a finite-dimensional Bayesian formulation.

Page Thumbnails

  • Thumbnail: Page 
[1]
    [1]
  • Thumbnail: Page 
2
    2
  • Thumbnail: Page 
3
    3
  • Thumbnail: Page 
4
    4
  • Thumbnail: Page 
5
    5
  • Thumbnail: Page 
6
    6
  • Thumbnail: Page 
7
    7
  • Thumbnail: Page 
8
    8
  • Thumbnail: Page 
9
    9
  • Thumbnail: Page 
10
    10
  • Thumbnail: Page 
11
    11
  • Thumbnail: Page 
12
    12
  • Thumbnail: Page 
13
    13
  • Thumbnail: Page 
14
    14
  • Thumbnail: Page 
15
    15
  • Thumbnail: Page 
16
    16
  • Thumbnail: Page 
17
    17
  • Thumbnail: Page 
18
    18
  • Thumbnail: Page 
19
    19
  • Thumbnail: Page 
20
    20
  • Thumbnail: Page 
21
    21
  • Thumbnail: Page 
22
    22
  • Thumbnail: Page 
23
    23
  • Thumbnail: Page 
24
    24
  • Thumbnail: Page 
25
    25
  • Thumbnail: Page 
26
    26
  • Thumbnail: Page 
27
    27
  • Thumbnail: Page 
28
    28
  • Thumbnail: Page 
29
    29
  • Thumbnail: Page 
30
    30
  • Thumbnail: Page 
31
    31
  • Thumbnail: Page 
32
    32
  • Thumbnail: Page 
33
    33
  • Thumbnail: Page 
34
    34
  • Thumbnail: Page 
35
    35
  • Thumbnail: Page 
36
    36
  • Thumbnail: Page 
37
    37
  • Thumbnail: Page 
38
    38
  • Thumbnail: Page 
39
    39
  • Thumbnail: Page 
40
    40
  • Thumbnail: Page 
41
    41
  • Thumbnail: Page 
42
    42
  • Thumbnail: Page 
43
    43
  • Thumbnail: Page 
44
    44
  • Thumbnail: Page 
45
    45
  • Thumbnail: Page 
46
    46
  • Thumbnail: Page 
47
    47
  • Thumbnail: Page 
48
    48
  • Thumbnail: Page 
49
    49
  • Thumbnail: Page 
50
    50
  • Thumbnail: Page 
51
    51
  • Thumbnail: Page 
52
    52