Confidence intervals for the mean of the delta-lognormal distribution

Data that are skewed and contain a relatively high proportion of zeros can often be modelled using a delta-lognormal distribution. We consider three methods of calculating a 95% confidence interval for the mean of this distribution, and use simulation to compare the methods, across a range of realistic scenarios. The best method, in terms of coverage, is that based on the profile-likelihood. This gives error rates that are within 1% (lower limit) or 3% (upper limit) of the nominal level, unless the sample size is small and the level of skewness is moderate to high. Our results will also apply to the delta-lognormal linear model, when we wish to calculate a confidence interval for the expected value of the response variable, given the value of one or more explanatory variables. We illustrate the three methods using data on red cod densities, taken from a fisheries trawl survey in New Zealand.

[1]  D. J. Finney,et al.  The truncated binomial distribution. , 1949, Annals of eugenics.

[2]  D. J. Finney On the Distribution of a Variate Whose Logarithm is Normally Distributed , 1941 .

[3]  J. Aitchison On the Distribution of a Positive Random Variable Having a Discrete Probability Mass at the Origin , 1955 .

[4]  L. Jacobson,et al.  Indices of Relative Abundance from Fish Spotter Data based on Delta-Lognormal Models , 1992 .

[5]  Michael Pennington,et al.  Efficient Estimators of Abundance, for Fish and Plankton Surveys , 1983 .

[6]  D. Lindenmayer,et al.  Modelling the abundance of rare species: statistical models for counts with extra zeros , 1996 .

[7]  Anthony C. Davison,et al.  Bootstrap Methods and Their Application , 1998 .

[8]  Pierre Pepin,et al.  The robustness of lognormal-based estimators of abundance , 1990 .

[9]  Jon Helge Vølstad,et al.  Optimum size of sampling unit for estimating the density of marine populations , 1991 .

[10]  Stephen J. Smith Use of Statistical Models for the Estimation of Abundance from Groundfish Trawl Survey Data , 1990 .

[11]  R. Mead,et al.  The Design of Experiments: Statistical Principles for Practical Applications. , 1989 .

[12]  D A Berry,et al.  Logarithmic transformations in ANOVA. , 1987, Biometrics.

[13]  S. Moolgavkar,et al.  A Method for Computing Profile-Likelihood- Based Confidence Intervals , 1988 .

[14]  Gunnar Stefánsson,et al.  Analysis of groundfish survey abundance data: combining the GLM and delta approaches , 1996 .

[15]  Stephen J. Smith Evaluating the efficiency of the δ-distribution mean estimator , 1988 .

[16]  C. Land,et al.  An Evaluation of Approximate Confidence Interval Estimation Methods for Lognormal Means , 1972 .

[17]  ON TESTING THE ROBUSTNESS OF LOGNORMAL-BASED ESTIMATORS , 1991 .

[18]  Ganapati P. Patil,et al.  The gamma distribution and weighted multimodal gamma distributions as models of population abundance , 1984 .