1. Length-Biased Sampling: A Review of Applications Termeh Shafie Department of Statistics Department of Statistics Umeå University termeh.shafie@stat.umu.se

2. Outline 1. Length-Biased Sampling & the Estimation-Problem 2. Applications & Suggested Solutions 3. Simulation under Misspecified Sampling Inclusion Probabilities

3. Length-Biased Sampling The probability of sample inclusion of a population unit is related to the value of the variable measured. The probability of sample inclusion of a population unit is related to the value of the variable measured. Cox (1969): Textile fibre sampling Cox (1969): Textile fibre sampling A simple illustration of the problem when estimating the population mean A simple illustration of the problem when estimating the population mean

4. The Estimation Problem Assume there is a population with elements The mean of the population is

5. The Estimation Problem Suppose observations form a sample with sample mean where if individual i is sampled otherwise

6. The Estimation Problem The expected value of the sample mean is where are the inclusion probabilities of the population units.

7. The Estimation Problem Using simple random sampling and thus

8. The Estimation Problem However in general is unknown and thus The sample mean becomes a biased estimator of the population mean.

9. Cox (1969) Derived the length-biased or weighted pdf and looked at the estimation of the population mean from a length-biased sample. Assume is a random sample with pdf

10. Cox (1969) It can be shown that An unbiased estimator of is

11. Cox (1969) with variance with varianceNote: ~ N

12. Cox (1969) Relation between the moments of g(x) and f(x) : The relative bias is thus

14. 2. APPLICATIONS Technical/Industrial Sampling Cox (1969): Sampling textile fibres and the estimation of fibre length distribution.

15. Marketing Shopping Center Sampling & Mall Intercept Surveys: Shopping Center Sampling & Mall Intercept Surveys: - Keillor et al (2001): Global consumer tendencies. - Sudman (1980): Quota sampling techniques and weighting procedures to correct for frequency bias. - Nowell et al (1991): correction techniques for length- biased sampling in two situations; when total length of stay is known or estimated and when only the recurrence time is known.

16. Epidemiology Sampling procedure for the collection of positive-valued or lifetime data are length- biased (Simon 1980, Zelen et al. 1969) Sampling procedure for the collection of positive-valued or lifetime data are length- biased (Simon 1980, Zelen et al. 1969) Wang (1996): statistical analysis of length- biased data under proportional hazards model. A pseudo-likelihood approach for estimation of the parameters from length-biased data is presented. Wang (1996): statistical analysis of length- biased data under proportional hazards model. A pseudo-likelihood approach for estimation of the parameters from length-biased data is presented.

17. Resource Economics On-site sampling: On-site sampling: - Deriving demand functions for a recreational site (Bockstael 1990, Ovaskainen et al. 2001) - Charting trip taking behavior (Bowker 1998) - Travel cost models of recreational demand (Moons et al. 2001) - Contingent valuation surveys for the elicitation of non-market goods (Cameron et al. 1987, Nowell et al. 1988)

18. Resource Economics Shaw (1988): Three problems with on-site samples’ regression; Shaw (1988): Three problems with on-site samples’ regression; 1. Non-negative integers 2. Truncation 3. Endogeneous Stratification

19. Resource Economics Shaw (1988): recreational demand modeling under two assumptions about the dependent variable’s distribution: 1. Normal distribution 2. Poisson distribution: y=1,2,…

20. Resource Economics Englin & Shonkwiler (1995): Englin & Shonkwiler (1995): - The Negative Binomial Model The truncated, stratified model is y=1,2,…

21. Resource Economics Nunes (2003): Binary Choice Models The count variable is described by a Poisson distribution with an unobservable heterogeneity term correlated with the error term in a probit binary choice model

22. 3. Misspecification of Sampling Probabilities: A Simulation Aim: Aim: To see whether or not the effect of missepecified sampling probabilities is large or not… What happens if time per visit is correlated with frequency of visits when estimating the expected number of visits? What happens if time per visit is correlated with frequency of visits when estimating the expected number of visits?

23. Misspecification of Sampling Probabilities: A Simulation Time is modeled as a function of frequency of visits when estimating the population mean. Time is modeled as a function of frequency of visits when estimating the population mean. ~ Poisson ~ Poisson ~ Exponential ~ Exponential ~ Gamma ~ Gamma The inclusion probabilities are proportional to the time spent at the site:

24. Misspecification of Sampling Probabilities: A Simulation The three estimators used for the simulation are: The sample mean: The sample mean: Shaw’s estimator: Shaw’s estimator: Cox’s Estimator: Cox’s Estimator:

25. Simulation Results Sample mean 0.689 0.964 1.058 0.689 0.964 1.058 (0.481) (0.939) (1.131) 0.780 0.983 1.118 (0.656) (1.016) (1.301) Shaw’s estimator -0.311 -0.036 0.058 (0.103) (0.011) (0.015) -0.220 - 0.017 0.118 (0.096) (0.050) (0.065) Cox’s estimator 0.398 0.567 0.642 0.398 0.567 0.642 (0.162) (0.327) (0.419) 0.155 0.036 0.176 0.155 0.036 0.176 (0.100) (0.081) (0.112)

26. Summary If the probabilities of sample inclusion of population units are related to the values of the variable measured, the parameter estimates will be biased and inconsistent. Thus correctly specified sampling inclusion mechanisms should not be neglected!

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28. And finally she stops…