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  • Author or Editor: Jonnie G. Medina x
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Paul W. Mielke Jr. and Jonnie G. Medina

Abstract

A new covariate ratio procedure is presented for estimating a treatment-induced effect. The procedure 1) allows for uncontrolled natural variability, 2) adjusts for disproportionate allocation of non-treated and treated experimental units, 3) diminishes the influence in an objective manner of an individual value corresponding to any experimental unit, and 4) accounts for differential treatment effects, i.e., a simple location or scale change is not assumed. This procedure is applied to specific meteorologically defined partitions involving data of the Climax I and II experiments. Results based on the pooled data indicate a 32% precipitation increase for the −20 to − 11°C 500 mb temperature partition, a 49% increase for the 190 to 250° 700 mb wind direction partition, and a 13% increase for the total sample. Comparisons based on Monte Carlo simulations (re-randomization) indicate that this procedure yields estimates which are more stable (precise) than corresponding estimates based on the double ratio.

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Paul W. Mielke Jr., Kenneth J. Berry, and Jonnie G. Medina

Abstract

The Climax I and II wintertime orographic cloud seeding experiments have recently been reanalyzed (Mielke et al., 1981c). The primary inference technique of this recent reanalysis involved (i) target-control residual data resulting from a simple linear model fitted by least squares and (ii) analyses based on classical linear rank statistics. The respective problems associated with this inference technique are (i) the residual data are highly dependent on a few very large values due to the model being fit by least squares and (ii) the complex non-Euclidean geometry underlying classical linear rank tests. The purpose of this article is to describe and apply a new inference technique which resolves both of these problems. This new inference technique involves residual data resulting from a median regression line and analyses based on recently developed rank tests associated with multi-response permutation procedures (MRPP). Application of this new inference to specific meteorological partitions of the Climax I and II experiments indicates that the evidence for a seeding effect is a little stronger (i.e., smaller P values) with the new technique than with the old technique for the warm Climax II 500 mb temperature partition and a Climax I 700 mb wind velocity partition, a little weaker for the warm Climax II 700 mb equivalent potential temperature partition, and about the same for the other meteorological partitions examined.

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