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1 A Resampling Study of NASS Survey MPPS Sampling Strategy By Stanley Weng National Agricultural Statistics Service U.S. Department of Agriculture.

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Presentation on theme: "1 A Resampling Study of NASS Survey MPPS Sampling Strategy By Stanley Weng National Agricultural Statistics Service U.S. Department of Agriculture."— Presentation transcript:

1 1 A Resampling Study of NASS Survey MPPS Sampling Strategy By Stanley Weng National Agricultural Statistics Service U.S. Department of Agriculture

2 2 INTRODUCTION MPPS Multivariate Probability Proportional to Size Address multiple, and often competing, purposes (multi targets) of a survey Used for NASS Crops Survey (CS) etc., since 1999

3 3 MPPS Technically Sample was selected using a Poisson method. Each farm i had a unique probability of selection, formed by

4 4 MPPS where is the item m selection probability, determined by auxiliary data with the assumption of the variance proportional to (a power of) the auxiliary variable value optimal allocation a desired item-level sample size

5 5 MPPS Development and application of the MPPS strategy at NASS: Amrhein, Hicks and Kott (1996) Amrhein and Bailey (1998) Bailey and Kott (1997) Hicks, Amrhein and Kott (1996) Kott, Amrhein and Hicks (1998).

6 6 A COMPARISON STUDY This study was designed to compare MPPS with the previously used SRS ((Stratified) Simple Random Sampling) strategy

7 7 THIS STUDY Explored the resampling approach to reveal the statistical characteristics/ behavior of NASS Ag survey data Raised issues for further investigation to improve our understanding and practice of NASS Ag survey sampling /estimation

8 8 RESAMPLING Population bootstrap Base sample June Crop Survey MPPS samples Pseudo population Composed of replicates of base sample elements, according to the (integerized) weight of the element

9 9 RESAMPLING Resamples Independent samples, drawn from by Poisson and SRS sampling strategies respectively

10 10 RESAMPLING Resample totals, and

11 11 RESAMPLING Resampling variance estimate for the sample total estimate Bootstrap statistic

12 12 DATA The crop component of the 2004 and 2005 June QAS, for all 42 participating states Certainty elements were eliminated from sample, to avoid unnecessary complication,

13 13 RESAMPLING VAR ESTIMATES Based on 1000 resamples Naive Comparison Log-Log Plot Resampling variance est vs sample total across crops – for each state Overlay: Poisson (*) vs SRS (^)

14 14 Naive Comparison General linear trend (Assumption: the variance proportional to a power of the total) For majority of crops, SRS variance appeared greater than Poisson variance (but often not appreciably)

15 15 Log-Log Plot of Resampling Variance Est vs Total Across Crops: CA Overlay: Poisson (*) vs SRS (^) pot srg saf sun dwh bar ctp oat ohy ctu wwh ric crn alf

16 16 Validness of the Comparison Need additional information to justify The quality of the resampling variance estimate depends on the statistical quality of the resample totals, which also provides evidence for the appropriateness of the sampling strategy Among various aspects, the most important: NORMALITY

17 17 Normality Q – Q plot of resample totals Demonstration: CA Most crops: Good shape of Q-Q Plot (Corn, Potatoes) Exception: Other Hay Evidence that Poisson was better than SRS

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24 24 Outliers on the log-log plot Located far apart from the general trend The two sampling strategies gave appreciably different estimates Demonstration: CA: Other Hay MT: Potatoes Evidence that SRS was better

25 25 Log-Log Plot of Resampling Variance Est vs Total Across Crops: MT Overlay: Poisson (*) vs SRS (^) mus sun can pot saf fla crn oat ohy dwh bar alf wwh swh

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28 28 FINITE SAMPLE RESAMPLING Complexities - Due to the special features of survey sampling Nonindependence arising in sampling without replacement Other complexities of finite population structure by designs and estimators

29 29 FINITE SAMPLE RESAMPLING Effects of discreteness (Davison & Hinkley, 1997, 2.3.2) Discrete empirical distribution and in particular, In finite population sampling, the pseudo population formed by replicates of sample elements

30 30 FINITE SAMPLE RESAMPLING Issues with this study Comparable sample size - Addressed by size adjustment Impact of the base sample - Not clear

31 31 Impact of Base Sample For finite population resampling, the general guideline The resampling population mimics the original population, and The resamples, mimic the base sample, drawn from by a design identical to the one by which the base sample was originally drawn (Sarndal, et al., 1992, Ch. 11)

32 32 AT ISSUE How the resampling technique should be correctly modified to accommodate the finite sampling situation?

33 33 AT ISSUE In literature, most reported finite sample resampling studies used (stratified) SRS, which bears the most similarity to the infinite population independent random sampling - the standard setting that the resampling technique is based on

34 34 SUMMARY An Approach Resampling & analysis of resamples, using statistical graphical and diagnostic techniques, to reveal statistical characteristics / behavior of NASS Ag survey data

35 35 SUMMARY Sampling strategy comparison Poisson seemed to be preferable to stratified simple random sampling A national comparison table of the two strategies across crops and states is to be produced for a comprehensive picture with likely causal factors identified

36 36 FURTHER INVESTIGATION To develop statistical understanding, the resampling setting of this study and other statistical information techniques will be further explored

37 37 FURTHER INVESTIGATION Behavior of Studentized bootstrap statistics Statistical function (Booth, Butler, and Hall, 1994; Davison & Hinkley, 1997) Examine different survey data


39 39 ALFAlfalfa All Harvested Acres BAR Barley All Planted Acres CAN Canola All Planted Acres CRN Corn Planted Acres CTP Pima Cotton Planted Acres CTU Upland Cotton Planted Acres DEB Dry Beans Planted Acres DWH Durum Wheat Planted Acres FLA Flaxseed Planted Acres MUS Mustard All Planted Acres OAT Oats All Planted Acres OHY Other Hay Harvested Acres PNT Peanuts All Planted Acres POT Potatoes All Planted Acres RIC Rice All Grain Planted Acres RYE Rye All Planted Acres SAF Safflower All Planted Acres SGBSugarcane All Planted Acres* SOY Soybeans All Planted Acres SPT Sweet potatoes Planted Acres SRG Sorghum All Planted Acres SUG Sugarcane For Sugar Harvested Acres SUN Sunflowers All Planted Acres SWH Spring Wheat Irr Planted Acres WWHWinter Wheat All Planted Acres

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