Www.ifa-ulm.de Extension, Compression, and Beyond A Unique Classification System for Mortality Evolution Patterns September, 8 th 2015 Martin Genz Joint.

Slides:



Advertisements
Similar presentations
Learning Objectives In this chapter you will learn about measures of central tendency measures of central tendency levels of measurement levels of measurement.
Advertisements

G. Alonso, D. Kossmann Systems Group
Measures of Dispersion
Gene selection using Random Voronoi Ensembles Stefano Rovetta Department of Computer and Information Sciences, University of Genoa, Italy Francesco masulli.
Chapter 3 Producing Data 1. During most of this semester we go about statistics as if we already have data to work with. This is okay, but a little misleading.
BCOR 1020 Business Statistics Lecture 17 – March 18, 2008.
Marakas: Decision Support Systems, 2nd Edition © 2003, Prentice-Hall Chapter Chapter 4: Modeling Decision Processes Decision Support Systems in the.
1 Validation and Verification of Simulation Models.
MULTIPLE INTEGRALS Double Integrals over General Regions MULTIPLE INTEGRALS In this section, we will learn: How to use double integrals to.
Copyright © 2010, 2007, 2004 Pearson Education, Inc. Lecture Slides Elementary Statistics Eleventh Edition and the Triola Statistics Series by.
1 Cornerstones of Managerial Accounting, 2e Copyright © 2008 Thomson South-Western, a part of the Thomson Corporation. Thomson, the Star logo, and South-Western.
1 CHAPTER M4 Cost Behavior © 2007 Pearson Custom Publishing.
Chapter 8 Experimental Research
This Week: Testing relationships between two metric variables: Correlation Testing relationships between two nominal variables: Chi-Squared.
381 Descriptive Statistics-III (Measures of Central Tendency) QSCI 381 – Lecture 5 (Larson and Farber, Sects 2.3 and 2.5)
Multiple Integrals 12. Double Integrals over General Regions 12.3.
Copyright © Cengage Learning. All rights reserved Double Integrals over General Regions.
DOUBLE INTEGRALS OVER GENERAL REGIONS
© Jorge Miguel Bravo 1 Eurostat/UNECE Work Session on Demographic Projections Lee-Carter Mortality Projection with "Limit Life Table" Jorge Miguel Bravo.
Enhanced Annuities, Individual Underwriting, and Adverse Selection August 6, Enhanced Annuities, Individual Underwriting, and Adverse Selection.
A Survey of Probability Concepts Chapter 5. 2 GOALS 1. Define probability. 2. Explain the terms experiment, event, outcome, permutations, and combinations.
Discussion: Risk and Valuation of Contingent Catastrophe Bonds by Daniel Bauer and Florian Kramer Discussion by Patrick Brocket Longevity 5: Fifth International.
Central Statistical Office ZIMBABWE DATA ANALYSIS AND INTERPRETATION OF 2004 LFS Lovemore Sungano Ziswa.
1 CSI5388: Functional Elements of Statistics for Machine Learning Part I.
The Practice of Statistics Third Edition Chapter 4: More about Relationships between Two Variables Copyright © 2008 by W. H. Freeman & Company Daniel S.
1 Introduction to Estimation Chapter Concepts of Estimation The objective of estimation is to determine the value of a population parameter on the.
Lecture 12 Statistical Inference (Estimation) Point and Interval estimation By Aziza Munir.
Copyright © 2005 Brooks/Cole, a division of Thomson Learning, Inc. 1 Chapter 4 Numerical Methods for Describing Data.
© 2008 Brooks/Cole, a division of Thomson Learning, Inc. 1 Chapter 4 Numerical Methods for Describing Data.
Chapter 3 Discrete Time and State Models. Discount Functions.
Investigation into evidence for economies of scale in the water and sewerage industry in England and Wales Presentation of Findings Water UK City Conference,
Quality Control Lecture 5
1 Mortality Compression and Longevity Risk Jack C. Yue National Chengchi Univ. Sept. 26, 2009.
The Chicago Guide to Writing about Multivariate Analysis, 2 nd edition. Criteria for choosing a reference category Jane E. Miller, PhD.
1 Chapter 4: Describing Distributions 4.1Graphs: good and bad 4.2Displaying distributions with graphs 4.3Describing distributions with numbers.
Helmholtzstraße 22 D Ulm phone+49 (0) 731/ fax +49 (0) 731/ It Takes Two: Why Mortality Trend Modeling is more.
Copyright © 2010, 2007, 2004 Pearson Education, Inc. All Rights Reserved. Section 7-1 Review and Preview.
Design and Assessment of the Toronto Area Computerized Household Activity Scheduling Survey Sean T. Doherty, Erika Nemeth, Matthew Roorda, Eric J. Miller.
A Survey of Probability Concepts
Chapter 7 Energy of a System.
Introduction to Conjoint Analysis.. Different Perspectives, Different Goals Buyers: Most desirable features & lowest price Sellers: Maximize profits by:
Chapter 3: Maximum-Likelihood Parameter Estimation l Introduction l Maximum-Likelihood Estimation l Multivariate Case: unknown , known  l Univariate.
© 2011 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part, except for use as permitted in a license.
C82MST Statistical Methods 2 - Lecture 1 1 Overview of Course Lecturers Dr Peter Bibby Prof Eamonn Ferguson Course Part I - Anova and related methods (Semester.
1 Chapter 4 Numerical Methods for Describing Data.
Chapter 6: Interpreting the Measures of Variability.
INTERNATIONAL MIGRATION DATA as input for population projections Anne HERM and Michel POULAIN Estonian Interuniversity Population Research Centre, Estonia.
Probability Concepts. 2 GOALS 1. Define probability. 2. Explain the terms experiment, event, outcome, permutations, and combinations. 3. Define the terms.
2007 Annual Meeting ● Vancouver IP 40 LTD & STD Pricing ● Andrew Ryan 2007 Annual Meeting ● Vancouver IP 40 LTD & STD Pricing ● Andrew Ryan Canadian Institute.
Modeling K The Common Core State Standards in Mathematics Geometry Measurement and Data The Number System Number and Operations.
Warsaw Summer School 2015, OSU Study Abroad Program Normal Distribution.
Discussion of Mortality Compression and Longevity Risk Discussed by Yijia Lin University of Nebraska - Lincoln Fifth International Longevity Risk and Capital.
Gender Statistics in Georgia Georgia. 3-4 November, 2015 Chisinau, Moldova.
1 STAT 500 – Statistics for Managers STAT 500 Statistics for Managers.
Presented to Toll Modeling Panel presented by Krishnan Viswanathan, Cambridge Systematics, Inc.. September 16, 2010 Time of Day in FSUTMS.
CORRELATION-REGULATION ANALYSIS Томский политехнический университет.
Methods of multivariate analysis Ing. Jozef Palkovič, PhD.
BAE 6520 Applied Environmental Statistics
BAE 5333 Applied Water Resources Statistics
Chapter 11 Chi-Square Tests.
Partial Derivative - Definition
Dynamical Statistical Shape Priors for Level Set Based Tracking
Numerical Descriptive Measures
Maximal Independent Set
Process Capability.
Chapter 11 Chi-Square Tests.
Chapter 11 Chi-Square Tests.
Lecture 8 Nash Equilibrium
Chapter 12 Statistics.
Presentation transcript:

Extension, Compression, and Beyond A Unique Classification System for Mortality Evolution Patterns September, 8 th 2015 Martin Genz Joint work with Matthias Börger and Jochen Ruß Institute for Finance and Actuarial Sciences and University of Ulm, Germany

Agenda Key question Classification of mortality evolutions in the past Shortcomings A new classification framework Requirements Details Application Summary 2© September 2015Extension, Compression, and Beyond

Key question Life expectancy increases in many countries. But changes in life expectancy (and other typically used statistics) are only a consequence of the underlying change of the age distribution of deaths. Key question: How does the shape of these curves change over time? 3© September 2015Extension, Compression, and Beyond

Classification of mortality evolutions in the past Shortcomings There exists a variety of literature on the question how the age distribution of deaths changes over time. We have identified some shortcomings there: Different notions for certain observations have been established but often these scenarios were defined imprecisely, e.g.: compression (≈ vertical deformation of the deaths curve) extension (≈ horizontal deformation of the deaths curve) rectangularization (≈ survival curve becomes more and more rectangular) … Some of these scenarios were supposed to be mutually exclusive, but there are counterexamples. Several often used statistics are insufficient or even misleading. Often effects caused by the choice of a certain age range under observation were not considered. In our paper, we give some examples for each of these shortcomings. 4© September 2015Extension, Compression, and Beyond

Classification of mortality evolutions in the past Shortcomings Imprecise scenario definitions: E.g., rectangularization is defined by a final state. 5© September 2015Extension, Compression, and Beyond

Classification of mortality evolutions in the past Shortcomings Exclusiveness of scenarios: E.g., compression and shifting mortality are assumed to be opposing scenarios. Neither compression nor shifting mortality prevail. Compression and shifting mortality coexist. 6© September 2015Extension, Compression, and Beyond

Classification of mortality evolutions in the past Shortcomings Insufficient or misleading statistics: Example 1: compression cannot always be detected by an exclusive analysis of M and SD(M+). Example 2: compression cannot always be detected with IQR 7© September 2015Extension, Compression, and Beyond IQR constant

Classification of mortality evolutions in the past Shortcomings The choice of the age range matters: The age range should be chosen depending on the question at hand. 8© September 2015Extension, Compression, and Beyond s(65)=1 compression into the higher ages no compr. within the higher ages

A new classification framework Requirements In light of these shortcomings of previous approaches, we postulate that a new classification system should… … capture every observed mortality evolution, … allow for mixed scenarios, … be applicable to different age ranges, … build on statistics that can be feasibly calculated and easily interpreted, … be extendable by additional components if needed. Our new approach: We use the deaths curve as basis for the framework. We define 4 characteristics of the deaths curve for a unique classification of observed mortality evolutions. 9© September 2015Extension, Compression, and Beyond

A new classification framework Details 10© September 2015Extension, Compression, and Beyond upper bound of the death curve’s support UB: extension or contraction modal age at death M: right or left shift degree of inequality DoI: compression or decompression number of deaths in the modal age at death d(M): concentration or diffusion

A new classification framework Details Each scenario is defined by a 4-dimensional vector where each component can have three specifications: This allows for 3 4 =81 different scenarios (some of which might not be relevant in practice) The framework satisfies the requirements: Each observed mortality evolution can uniquely be classified in one of those scenarios. Pure and mixed scenarios are included. The framework can be applied to age ranges starting at any given age up to UB. Feasible and easily interpretable statistics are used. The framework is extendable by additional statistics if needed. In the paper, we discuss different issues in estimating these statistics, e.g. how to estimate UB. 11© September 2015Extension, Compression, and Beyond componentattainable states Mright shift / neutral / left shift UBextension / neutral / contraction DoIcompression / neutral / decompression d(M)concentration / neutral / diffusion

A new classification framework Application: The mortality evolution of Swedish females age range 10 to UB: Each component of the vector develops independently from the others (no redundant information). We observe mixed scenarios (rather the rule than an exception). age range 60 to UB: We observe different scenarios for different age ranges (age range matters). In the paper, we analyze this application in more detail. 12© September 2015Extension, Compression, and Beyond Example

Summary In the paper, we have… … identified shortcomings of previous approaches for classification of mortality scenarios, … derived requirements for a new framework, … identified 4 central characteristics of the deaths curve, … derived a new classification framework based on these characteristics, which … builds on clear scenario definitions, … provides a unique classification for each mortality evolution, … allows for mixed scenarios, … is applicable for different age ranges, … applied the framework to concrete data. 13© September 2015Extension, Compression, and Beyond

Thank you for your attention! Martin Genz (M.Sc.) +49 (731) © September Extension, Compression, and Beyond