Environmental and Ecological Statistics

, Volume 1, Issue 4, pp 287–302

Small sample estimation for Taylor's power law

Authors

  • S. J. Clark
    • IACR, Statistics DepartmentsRothamsted Experimental Station
  • J. N. Perry
    • Entomology and Nematology DepartmentsRothamsted Experimental Station
Papers

DOI: 10.1007/BF00469426

Cite this article as:
Clark, S.J. & Perry, J.N. Environ Ecol Stat (1994) 1: 287. doi:10.1007/BF00469426

Abstract

An analysis of counts of sample size N=2 arising from a survey of the grass Bromus commutatus identified several factors which might seriously affect the estimation of parameters of Taylor's power law for such small sample sizes. The small sample estimation of Taylor's power law was studied by simulation. For each of five small sample sizes, N=2, 3, 5, 15 and 30, samples were simulated from populations for which the underlying known relationship between variance and mean was given by σ2 = cμd. One thousand samples generated from the negative binomial distribution were simulated for each of the six combinations of c=1,2 and 11, and d=1, 2, at each of four mean densities, μ=0.5, 1, 10 and 100, giving 4000 samples for each combination. Estimates of Taylor's power law parameters were obtained for each combination by regressing log10s2 on log10m, where s2 and m are the sample variance and mean, respectively. Bias in the parameter estimates, b and log10a, reduced as N increased and increased with c for both values of d and these relationships were described well by quadratic response surfaces. The factors which affect small-sample estimation are: (i) exclusion of samples for which m = s2 = 0; (ii) exclusion of samples for which s2 = 0, but m > 0; (iii) correlation between log10s2 and log10m; (iv) restriction on the maximum variance expressible in a sample; (v) restriction on the minimum variance expressible in a sample; (vi) underestimation of log10s2 for skew distributions; and (vii) the limited set of possible values of m and s2. These factors and their effect on the parameter estimates are discussed in relation to the simulated samples. The effects of maximum variance restriction and underestimation of log10s2 were found to be the most severe. We conclude that Taylor's power law should be used with caution if the majority of samples from which s2 and m are calculated have size, N, less than 15. An example is given of the estimated effect of bias when Taylor's power law is used to derive an efficient sampling scheme.

Keywords

biasnegative binomial distributionparameter estimationresponse surfacesample schemesimulationsmall samplesTaylor's power lawvariance-mean relationship

Copyright information

© Chapman & Hall 1994