Spatial cluster detection for censored outcome data

While numerous methods have been proposed to test for spatial cluster detection, in particular for discrete outcome data (e.g., disease incidence), few have been available for continuous data that are subject to censoring. This article provides an extension of the spatial scan statistic (Kulldorff, 1997, Communications in Statistics 26, 1481-1496) for censored outcome data and further proposes a simple spatial cluster detection method by utilizing cumulative martingale residuals within the framework of the Cox's proportional hazards models. Simulations have indicated good performance of the proposed methods, with the practical applicability illustrated by an ongoing epidemiology study which investigates the relationship of environmental exposures to asthma, allergic rhinitis/hayfever, and eczema.     

Use of the Gibbs Sampler to Obtain Conditional Tests, with Applications

A random sample is drawn from a distribution which admits aminimal sufficient statistic for the parameters. The Gibbs sampleris proposed to generate samples, called conditionally sufficientor co-sufficient samples, from the conditional distributionof the sample given its value of the sufficient statistic. Theprocedure is illustrated for the gamma distribution. Co-sufficientsamples may be used to give exact tests of fit; for the gammadistribution these are compared for size and power with approximatetests based on the parametric bootstrap.     

In vitro versus in vivo genetic divergence in potato

 The objective of this study was to compare the genetic divergence pattern in potato (Solanum tuberosum L.) under in vitro and in vivo conditions. Twenty two potato genotypes were evaluated for ten morphological characters under four in vitro conditions, and for 20 characters under four in vivo seasons. Mahalanobis’ generalized intra- and inter-group genetic distances, and the distribution of genotypes into different clusters, led to the same conclusions under both in vitro and in vivo conditions: (1) genetic diversity was not related to geographic diversity, (2) genetic distances were higher between Tuberosum and Andigena than within Tuberosum and Andigena, and (3) present-day Indian varieties have more resemblance to Tuberosum than to the Andigena group. The in vitro approach was more effective than the in vivo approach for differentiating the genotypes per se, although its effectiveness for cross prediction is known to be low. Received: 15 September 1997 / Accepted: 15 July 1998     

Extended Rank Analysis of Covariance as a More Efficient Matched Analysis Considering Trend Information

Classical matched analysis, regarded as analysis of covariance (ANOCOVA) in a broad sense, makes no attempt in modeling and may therefore be inefficient. In this paper, we discuss the relative efficiencies of the ERMP (extended rank and matched‐pair) test (Chen and Quade , 2000) to standard matched methods, and extend it to the case of multivariate covariables X . Taking advantage of trend information between the response Y and the covariables X by ranking after matching, ERMP test achieves better efficiency than a proposed class of weighted matched statistics. When Y is dichotomous, the optimal weighted matched statistic is equivalent to the Mantel‐Haenszel statistic. Example and simulation results also suggest the conclusion.     

On Second Order Efficiency of Estimators Derived by Generalised χ2 Distance Functions

Taylor (1953) proposed a distance function in connection with the logit χ² estimator. For product (associated) multinomial distributions, he showed that minimization of the distance function yields BAN estimators. Aithal (1986) and Rao (1989) considered a modified version of Taylor's distance function and showed that a member belonging to this class leads to a second order efficient estimator. In this paper we consider Taylor's distance function and show that a member belonging to this class produces a second order efficient estimator. In addition to the above two, the m.l. estimator is also second order efficient. In order to compare these three second order efficient estimators, the small sample variances of the estimators are estimated through a simulation study. The results indicate that the variance of the m.l. estimator is the smallest in most of the cases.     

Estimation in Longitudinal or Panel Data Models with Random‐Effect‐Based Missing Responses

Summary In studies with longitudinal or panel data, missing responses often depend on values of responses through a subject‐level unobserved random effect. Besides the likelihood approach based on parametric models, there exists a semiparametric method, the approximate conditional model (ACM) approach, which relies on the availability of a summary statistic and a linear or polynomial approximation to some random effects. However, two important issues must be addressed in applying ACM. The first is how to find a summary statistic and the second is how to estimate the parameters in the original model using estimates of parameters in ACM. Our study is to address these two issues. For the first issue, we derive summary statistics under various situations. For the second issue, we propose to use a grouping method, instead of linear or polynomial approximation to random effects. Because the grouping method is a moment‐based approach, the conditions we assumed in deriving summary statistics are weaker than the existing ones in the literature. When the derived summary statistic is continuous, we propose to use a classification tree method to obtain an approximate summary statistic for grouping. Some simulation results are presented to study the finite sample performance of the proposed method. An application is illustrated using data from the study of Modification of Diet in Renal Disease.     

Resistance of wild wheat to stripe rust: Predictive method by ecology and allozyme genotypes

From 114 accessions of wild emmer wheat from 11 sites in Israel, known for their allozymic variation (Nevo & al. 1982), individual genotypes were tested for resistance to one isolate of stripe rust both in the seedling stage in a growth chamber and in the adult plant stage in the field. The results indicate that resistance to stripe rust in seedlings and adults are significantly correlated (r_s = 0.40, p < 0.001). Genetic polymorphisms of resistance to stripe rust vary geographically and are predictable by climatic, as well as allozymic markers. Three variable combinations of rainfall, evaporation, and temperature explain significantly 0.40–0.53 of the spatial variance in disease resistance to stripe rust, suggesting the operation of natural selection. Several allozyme genotypes are significantly associated with disease resistance. We conclude that natural populations of wild emmer wheat in Israel contain large amounts of disease resistance genes. These populations could be effectively screened and then utilized by the phytopathologist for identifying resistant genotypes and producing new resistant cultivars.Patterns of Resistance of Wild Wheat to Pathogens in Israel II.