The casemoth, Liothula omnivoa (Psychidae : lepidoptera) : a thesis presented in partial fulfilment of the requirements for the degree of Master of Science in Zoology at Massey University

Liothula omnivora, one of the two known casemoths endemic to New Zealand, belongs to the Lepidopteran family Psychidae. It is distributed throughout the country, and can be found on a large number of host plants (see later). The other N.Z. casemoth, Orophora concolor, has been found on Wild Irishman and cassinias in the river beds of the South Island (Miller, 1955). L. omnivora was first described by Fereday in 1878, but Meyrick (1890) transferred it to the genus Oiketicus (Guilding, 1827) mis-spelling it Oeceticus. Dr. Allan Watson (1967, pers. comm.) of the British Museum (Natural History) considers that this species should belong in the genus Liothula and the writer has adopted Watson's view in calling it L. omnivora. The type of L. omnivora is in the Canterbury Museum, Christchurch (Entomologische Beihefte 4, Horn and Kahle, 1937). Descriptions of the external morphology of the adult male and female have been made by Fereday (1878), Meyrick (1890) and Hudson (1928). Fereday and Hudson also described the larva, the pupa has been described by Hudson and Quail (1901), and the appearance of the egg briefly noted by Hudson

Review of Consolidating Taiwan’s Democracy

The article reviews the book Consolidating Taiwan\u27s Democracy by John F. Copper

Attributing a probability to the shape of a probability density

We discuss properties of two methods for ascribing probabilities to the shape of a probability distribution. One is based on the idea of counting the number of modes of a bootstrap version of a standard kernel density estimator. We argue that the simplest form of that method suffers from the same difficulties that inhibit level accuracy of Silverman's bandwidth-based test for modality: the conditional distribution of the bootstrap form of a density estimator is not a good approximation to the actual distribution of the estimator. This difficulty is less pronounced if the density estimator is oversmoothed, but the problem of selecting the extent of oversmoothing is inherently difficult. It is shown that the optimal bandwidth, in the sense of producing optimally high sensitivity, depends on the widths of putative bumps in the unknown density and is exactly as difficult to determine as those bumps are to detect. We also develop a second approach to ascribing a probability to shape, using Muller and Sawitzki's notion of excess mass. In contrast to the context just discussed, it is shown that the bootstrap distribution of empirical excess mass is a relatively good approximation to its true distribution. This leads to empirical approximations to the likelihoods of different levels of ``modal sharpness,'' or ``delineation,'' of modes of a density. The technique is illustrated numerically.Comment: Published at http://dx.doi.org/10.1214/009053604000000607 in the Annals of Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical Statistics (http://www.imstat.org