turkey hunting survey project dongchu sun
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Turkey Hunting Survey Project Dongchu Sun University of Missouri-Columbia April 26, 2002. Jointly with. Chong He (UMC) Roger Woodard, Ohio State U. Steven L. Sheriff, MDC John Molitor, U of S. California - PowerPoint PPT PresentationTRANSCRIPT
Turkey Hunting Survey Project Dongchu Sun University of Missouri-Columbia
April 26, 2002
Jointly with
Chong He (UMC) Roger Woodard, Ohio State U. Steven L. Sheriff, MDC John Molitor, U of S. California Jacob Oleson, UMC Larry Vangilder, MDC
Where……
Where we were?
Where we are?
Where we go?
Where we were?
Preparation: 1995--1997 He, Sun, Sheriff…….
Pilot Study: 1997--1998 Woodard
Phase 1: 1998—2000 Woodard, Molitor……
Where we are?
Ph.D Dissertations R. Woodard , 1999 Bayesian Hierarchical Models for Hunting
Success Rates
J. Molitor, 2000, Bayesian Analysis for various order
restricted problems
Articles in Refereed Journals
He & Sun (1998). EES, 223-236.
Woodard, Sun, He & Sheriff (1999). JABES, 456-472.
He & Sun (2000). Biometrics, 360-367.
Data 1988, 1990, 1992, 1994, 1996 ,
1998 Spring Turkey Hunting Survey
Hunting success rates during the entired spring season in 1994 ---- He and Sun (1998).
Here we use 1996 data for illustration.
1996 Turkey Hunting Survey
Regulations in spring of 1996: Two weeks Buy license anywhere in Missouri May hunt anywhere in Missouri May kill one turkey per trip per week
A simple random survey collected # of trips in each county each week Birds taken in each county each week
Goals Estimates:
--- Hunting success rates per trip --- hunting pressure --- harvest
Accurate for State: --- permit buyers: 95,800 --- large sample sizes: 7,000 --- returned: 5005 (71.5%)
Likelihood (model the data)
#ijy
Naïve frequency estimate of p_ij
y_ij / n_ij
Problems
Post-stratification --- cann’t draw samples at count level
Small samples for some counties
--- county 72 (4 trips in week 1)
--- county 73 (3 trips in week 1)
--- county 35 (0 trips in both weeks)
Method 1: A beta-gamma prior
Remarks on the Binomial- beta-gamma model
Good properties --- better than the naïve frequency
estimators --- quite robust in terms of choices of
(a_i, b_i) and (c_i,d_i).
Problems
--- contiguity between regions ? --- hard to include covariates
Method 2: Hierarchical linear mixed model
Stage 2 prior for Method 2
Stage 3 prior for Method 2
Method 2
Computation via MCMC
Calculation of posterior via integration is infeasible
Iterative method Produce random sample from
joint posterior distribution
Model Fitting
Hyperparameters:
Estimates
Robustness in changing hyperparameters
Model Comparison
Want to examine if Method 2 can be simplified.
AR or CAR
He and Sun (2000), AR and CAR are equally good for estimating p_ij
Improving estimates by adding covariates?
Woodard, Sun, He, and Sherif (1999) considered
Ramdom Trips n_ij Currently we have checkstation data k_ij : # killed turkeys in week I and county j Woodard (1999) modeled random trips
Unde the same prior for p_ij, the estimates of p_ij does not seem ``spatially’’ correlated as these when n_ij are treated as fixed.
Estimating k_ij
If we can estimate k_ij, could remove check stations.
Woodard, He and Sun (2001) assumed random trips n_ij,
Where we go?
Can we simultaneously estimate total # of trips, harvest, hunting success rates at county level?
How about pre-stratification? Properties of hiecarchical lines
mixed models?
--- Molitor, Sun and He (2000)……
Comments
Hierarchical Bayes models are superior to simple frequency estimates.
Normal linear mixed priors are superior to beta-gamma priors.
Spatial correlation among counties does exist.
More Comments
Include covariates such as forest coverage may not be helpful.
Assuming random trips will improve the estimates?
It can be used in a general small area estimation context.