The young adolescent crowd study (YACS) was conducted in order to look at the influence of various factors on use of controlled substances by middle school students. The contributing factors investigated were demographics (gender and race), self-esteem in different modalities such as school or athletic performance, and the peer group students belong to. Each student has a binary response for whether they have used alcohol, marijuana or cigarettes which was recorded in both seventh and eighth grade. Since the data has a binary repeated measures response, generalized estimating equations (GEE) in a logistic regression setting is a good way to model the data. The theory and method of GEE is explained in detail followed by results, issues encountered and a discussion of how the model worked with the data set.

Many attempts have been made to achieve successful recognition of handwritten digits. We report our results of using statistical method on handwritten digit recognition. A digitized handwritten numeral can be represented by an image with grayscales. The image includes features that are mapped into two-dimensional space with row and column coordinates. Based on this structure, two-dimensional penalized signal logistic regression (PSR) is applied to the recognition of handwritten digits. The data set is taken from the USPS zip code database that contains 7219 training images and 2007 test images. All the images have been deslanted and normalized into 16 x 16 pixels with various grayscales. The PSR method constructs a coefficient surface using a rich two-dimensional tensor product B-splines basis, so that the surface is more flexible than needed. We then penalize roughness of the coefficient surface with difference penalties on each coefficient associate with the rows and columns of the tensor product B-splines. The optimal penalty weight is found in several minutes of iterative operations. A competitive overall recognition error rate of 8.97% on the test data set was achieved. We will also review an artificial neural network approach for comparison. By using PSR, it requires neither long learning time nor large memory resources. Another advantage of the PSR method is that our results are obtained on the original USPS data set without any further image preprocessing. We also found that PSR algorithm was very capable to cope with high diversity and variation that were two major features of handwritten digits.

The ironwood tree (Casuarina equisetifolia), a protector of coastlines of the sub-tropical and tropical Western Pacific, is in decline on the island of Guam where aggressive data collection and efforts to mitigate the problem are underway. For each sampled tree the level of decline was measured on an ordinal scale consisting of five categories ranging from healthy to near dead. Several predictors were also measured including tree diameter, fire damage, typhoon damage, presence or absence of termites, presence or absence of basidiocarps, and various geographical or cultural factors. The five decline response levels can be viewed as categories of a multinomial distribution where the multinomial probability profile depends on the levels of these various predictors. Such data structure is well suited to a proportional odds model thereby leading to odds ratios involving cumulative probabilities which can be estimated and summarized using information from the predictor coefficient. Various modeling techniques were applied to address data set issues: reduced logistic models, spatial relationships of residuals using latitude and longitude coordinates, and correlation structure induced by the fact that trees were sampled in clusters at various sites. Among our findings, factors related to ironwood decline were found to be basidiocarps, termites, and level of human management.