SHORT-RUN INDICATORS OF FINANCIAL SUCCESS FOR SOUTHWEST MINNESOTA FARMERS

Agricultural Finance,

Quantile-Quantile Methodology -- Detailed Results

The linear quantile-quantile relationship provides an easy-to-implement yet effective tool for transformation to and testing for normality. Its good performance is verified in this report

Diurnal ocean surface layer model validation

The diurnal ocean surface layer (DOSL) model at the Fleet Numerical Oceanography Center forecasts the 24-hour change in a global sea surface temperatures (SST). Validating the DOSL model is a difficult task due to the huge areas involved and the lack of in situ measurements. Therefore, this report details the use of satellite infrared multichannel SST imagery to provide day and night SSTs that can be directly compared to DOSL products. This water-vapor-corrected imagery has the advantages of high thermal sensitivity (0.12 C), large synoptic coverage (nearly 3000 km across), and high spatial resolution that enables diurnal heating events to be readily located and mapped. Several case studies in the subtropical North Atlantic readily show that DOSL results during extreme heating periods agree very well with satellite-imagery-derived values in terms of the pattern of diurnal warming. The low wind and cloud-free conditions necessary for these events to occur lend themselves well to observation via infrared imagery. Thus, the normally cloud-limited aspects of satellite imagery do not come into play for these particular environmental conditions. The fact that the DOSL model does well in extreme events is beneficial from the standpoint that these cases can be associated with the destruction of the surface acoustic duct. This so-called afternoon effect happens as the afternoon warming of the mixed layer disrupts the sound channel and the propagation of acoustic energy

Variable Selection for 1D Regression Models

Variable selection, the search for j relevant predictor variables from a group of p candidates, is a standard problem in regression analysis. The class of 1D regression models is a broad class that includes generalized linear models. We show that existing variable selection algorithms, originally meant for multiple linear regression and based on ordinary least squares and Mallows’ Cp, can also be used for 1D models. Graphical aids for variable selection are also provided

Robust Regression with High Coverage

An important parameter for several high breakdown regression algorithm estimators is the number of cases given weight one, called the coverage of the estimator. Increasing the coverage is believed to result in a more stable estimator, but the price paid for this stability is greatly decreased resistance to outliers. A simple modification of the algorithm can greatly increase the coverage and hence its statistical performance while maintaining high outlier resistance

Inconsistency of Resampling Algorithms for High Breakdown Regression Estimators and a New Algorithm

Since high breakdown estimators are impractical to compute exactly in large samples, approximate algorithms are used. The algorithm generally produces an estimator with a lower consistency rate and breakdown value than the exact theoretical estimator. This discrepancy grows with the sample size, with the implication that huge computations are needed for good approximations in large high-dimensioned samples The workhorse for HBE has been the ‘elemental set’, or ‘basic resampling’ algorithm. This turns out to be completely ineffective in high dimensions with high levels of contamination. However, enriching it with a “concentration” step turns it into a method that is able to handle even high levels of contamination, provided the regression outliers are located on random cases. It remains ineffective if the regression outliers are concentrated on high leverage cases. We focus on the multiple regression problem, but several of the broad conclusions – notably those of the inadequacy of fixed numbers of elemental starts – are relevant to multivariate location and dispersion estimation as well. We introduce a new algorithm – the “X-cluster” method – for large high-dimensional multiple regression data sets that are beyond the reach of standard resampling methods. This algorithm departs sharply from current HBE algorithms in that, even at a constant percentage of contamination, it is more effective the larger the sample, making a compelling case for using it in the large-sample situations that current methods serve poorly. A multi-pronged analysis, using both traditional OLS and L1 methods along with newer resistant techniques, will often detect departures from the multiple regression model that can not be detected by any single estimator

Behavior of Elemental Sets in Regression

Elemental sets are used to produce trial estimates b of the regression coefficients β. If b0 minimizes ||b-β|| among all elemental fits b, then ||b0-β||=OP(n-1), regardless of the criterion used. For any estimator bA, ||bA-β|| is at best OP(n-1/2). Hence restricting fits to elemental introduces asymptotically negligible error

POSITIVE AFFECT PREDICTS FUTURE BODY MASS INDEX: A 2-YEAR PROSPECTIVE ANALYSIS OF THE AFRICAN AMERICAN HEALTH STUDY

poster abstractAlthough prospective studies indicate that negative affective factors (e.g., depression) predict increases in body mass index (BMI), few studies have examined whether positive affect is prospectively related to BMI in African Americans. Thus, it is unknown whether positive affect is related to BMI, independently of negative affect, for this ethnic group. This deficit in the literature is unfortunate, given that positive affect may protect against increases in BMI, and African Americans have among the highest rates of obesity (BMI ≥ 30 m/kg2). Accordingly, our objective was to determine whether positive affect predicts 2-year changes in BMI, independently of negative affective factors, in middle-aged African Americans. Participants were 674 African Americans aged 57-72 years who were enrolled in the African American Health study. For our study, all variables were measured in 2008 (baseline) and at 2-year follow-up. Positive affect was assessed using the 4-item positive affect subscale of the Center for Epidemiologic Studies-Depression Scale (CES-D), whereas depressive symptoms were assessed using the remaining CES-D items. Anxiety was measured using the GAD-7, and low vitality was assessed with the SF-36. Self-reported BMIs were used. Multiple linear regressions revealed that greater baseline positive affect predicted 2-year decreases in BMI (β = -.048, p = .026) after adjusting for age, sex, baseline BMI, depressive symptoms, anxiety, and low vitality. Depressive symptoms, anxiety, and low vitality did not predict BMI (ps > .10). Baseline BMI did not predict 2-year changes in positive affect (p = 55). Our findings suggest that positive affect may exert a protective effect against obesity in African Americans, whereas negative affective factors (i.e., depressive symptoms, anxiety, and low vitality) were unrelated to BMI in our sample. A key implication is that interventions for increasing positive affect in African Americans may be helpful in obesity prevention efforts for this at-risk population

Diagnosis of Some Model Deficiencies Using Recursive Residuals

1 online resource (PDF, 48 pages