Smoothing and forecasting mortality rates

The prediction of future mortality rates is a problem of fundamental importance for the insurance and pensions industry. We show how the method of P-splines can be extended to the smoothing and forecasting of two-dimensional mortality tables. We use a penalized generalized linear model with Poisson errors and show how to construct regression and penalty matrices appropriate for two-dimensional modelling. An important feature of our method is that forecasting is a natural consequence of the smoothing process. We illustrate our methods with two data sets provided by the Continuous Mortality Investigation Bureau, a central body for the collection and processing of UK insurance and pensions data.

[1]  Brian D. Marx,et al.  Generalized Linear Regression on Sampled Signals and Curves: A P-Spline Approach , 1999, Technometrics.

[2]  Matt P. Wand,et al.  Miscellanea. On the optimal amount of smoothing in penalised spline regression , 1999 .

[3]  Paul Dierckx,et al.  Curve and surface fitting with splines , 1994, Monographs on numerical analysis.

[4]  B. Marx,et al.  Multivariate calibration with temperature interaction using two-dimensional penalized signal regression , 2003 .

[5]  Ronald Lee,et al.  Modeling and forecasting U. S. mortality , 1992 .

[6]  M. Wand,et al.  Incorporation of historical controls using semiparametric mixed models , 2001 .

[7]  W. Cleveland,et al.  Locally Weighted Regression: An Approach to Regression Analysis by Local Fitting , 1988 .

[8]  G. Schwarz Estimating the Dimension of a Model , 1978 .

[9]  M. Wand,et al.  Respiratory health and air pollution: additive mixed model analyses. , 2001, Biostatistics.

[10]  D. Ruppert Selecting the Number of Knots for Penalized Splines , 2002 .

[11]  Clifford M. Hurvich,et al.  Smoothing parameter selection in nonparametric regression using an improved Akaike information criterion , 1998 .

[12]  Paul H. C. Eilers,et al.  Direct generalized additive modeling with penalized likelihood , 1998 .

[13]  María Durbán,et al.  A note on P-spline additive models with correlated errors , 2003, Comput. Stat..

[14]  S. Wood Thin plate regression splines , 2003 .

[15]  Arx,et al.  Generalized Linear Additive Smooth Structures , 2003 .

[16]  S. Wood,et al.  GAMs with integrated model selection using penalized regression splines and applications to environmental modelling , 2002 .

[17]  M. Wand,et al.  Simple Incorporation of Interactions into Additive Models , 2001, Biometrics.

[18]  G. Wahba,et al.  Semiparametric Analysis of Variance with Tensor Product Thin Plate Splines , 1993 .

[19]  Paul H. C. Eilers,et al.  Flexible smoothing with B-splines and penalties , 1996 .

[20]  Paul H. C. Eilers,et al.  Generalized linear regression on sampled signals and curves: a P -spline approach , 1999 .

[21]  Thomas C. M. Lee,et al.  Smoothing parameter selection for smoothing splines: a simulation study , 2003, Comput. Stat. Data Anal..

[22]  D Clayton,et al.  Models for temporal variation in cancer rates. II: Age-period-cohort models. , 1987, Statistics in medicine.

[23]  Matt P. Wand,et al.  Smoothing and mixed models , 2003, Comput. Stat..

[24]  C. R. Deboor,et al.  A practical guide to splines , 1978 .

[25]  Gerda Claeskens,et al.  Some theory for penalized spline generalized additive models , 2002 .

[26]  Michel Denuit,et al.  A Poisson log-bilinear regression approach to the construction of projected lifetables , 2002 .

[27]  M. Durbán,et al.  Flexible smoothing with P-splines: a unified approach , 2002 .

[28]  David Ruppert,et al.  Theory & Methods: Spatially‐adaptive Penalties for Spline Fitting , 2000 .

[29]  D Clayton,et al.  Models for temporal variation in cancer rates. I: Age-period and age-cohort models. , 1987, Statistics in medicine.

[30]  Paul H. C. Eilers,et al.  Fast and compact smoothing on large multidimensional grids , 2006, Comput. Stat. Data Anal..

[31]  X. Lin,et al.  Inference in generalized additive mixed modelsby using smoothing splines , 1999 .