NONLINEAR REGRESSION FOR SPLIT PLOT EXPERIMENTS

Split plot experimental designs are common in studies of the effects of air pollutants on crop yields. Nonlinear functions such the Weibull function have been used extensively to model the effect of ozone exposure on yield of several crop species. The usual nonlinear regression model, which assumes independent errors, is not appropriate for data from nested or split plot designs in which there is more than one source of random variation. The nonlinear model with variance components combines a nonlinear model for the mean with additive random effects to describe the covariance structure. We propose an estimated generalized least squares (EGLS) method of estimation for this model. The variance components are estimated two ways: by analysis of variance, and by an approximate MINQUE method. These methods are demonstrated and compared with results from ordinary nonlinear least squares for data from the National Crop Loss Assessment Network (NCLAN) program regarding the effects of ozone on soybeans. In this example all methods give similar point estimates of the parameters of the Weibull function. The advantage of estimated generalized least squares is that it produces proper estimates of the variances of the parameters and of estimated yields, which take the covariance structure into account. A computer program that fits the nonlinear model with variance components by the EGLS method is available from the authors

AUTOLOGISTIC MODEL OF SPATIAL PATTERN OF PHYTOPHTHORA EPIDEMIC IN BELL PEPPER: EFFECTS OF SOIL VARIABLES ON DISEASE PRESENCE

The pathogen Phytophthora capsici causes lesions on the crown, stem, and leaves of bell pepper, and rapidly causes the plant to die. The spatial patterns of disease in an agricultural field contain information about pathogen dispersal mechanisms and can be useful for developing methods of control of disease. Soil water content, soil pathogen population density, and disease incidence data were collected on a 20 x 20 grid in two naturally infested commercial bell pepper fields. In one field the initial pattern of disease closely matched the soil water content pattern and disease developed in areas where the pathogen population levels were high. In the other field no such correspondence was obvious from maps of disease and soil water content . The auto logistic model is a flexible model for predicting presence or absence of disease based on soil water content and soil pathogen population, while taking spatial correlation into account. In the autologistic model the log odds of disease in a particular quadrat are modeled as a linear combination of disease in neighboring quadrats and the soil variables. Neighboring quadrats can be defined as adjacent quadrats within a row, quadrats in adjacent rows, quadrats two rows away, and so forth. The regression coefficients give estimates of the increase in odds of disease if neighbors within a row or in adjacent rows show disease symptoms; thus we obtain information about the degree of spread in different directions. The coefficients for the soil variables give estimates of the increase in odds of disease as soil water content or pathogen population density increase. In this problem, soil water content is also highly correlated over quadrats. This introduces a kind of collinearity between water content and the disease in neighboring quadrats, making estimation and interpretation of the parameters of the auto logistic model more difficult. We discuss fitting and evaluating the autologistic model when the covariates are themselves spatially correlated

Avian response to microclimate in canopy gaps in a bottomland hardwood forest.

Abstract - Microclimate may infl uence use of early successional habitat by birds. We assessed the relationships between avian habitat use and microclimate (temperature, light intensity, and relative humidity) in experimentally created canopy gaps in a bottomland hardwood forest on the Savannah River Site, SC. Gaps were 2- to 3-year-old group-selection timber harvest openings of three sizes (0.13, 0.26, 0.50 ha). Our study was conducted from spring through fall, encompassing four bird-use periods (spring migration, breeding, post-breeding, and fall migration), in 2002 and 2003. We used mist netting and simultaneously recorded microclimate variables to determine the influence of microclimate on bird habitat use. Microclimate was strongly affected by net location within canopy gaps in both years. Temperature generally was higher on the west side of gaps, light intensity was greater in gap centers, and relative humidity was higher on the east side of gaps. However, we found few relationships between bird captures and the microclimate variables. Bird captures were inversely correlated with temperature during the breeding and postbreeding periods in 2002 and positively correlated with temperature during spring 2003. Captures were high where humidity was high during post-breeding 2002, and captures were low where humidity was high during spring 2003. We conclude that variations in the local microclimate had minor infl uence on avian habitat use within gaps. Instead, habitat selection in relatively mild regions like the southeastern US is based primarily on vegetation structure, while other factors, including microclimate, are less important

BOXCAR user and programmer manual, version 1.0

DTFH61-87-C-00032This Manual presents an overview, user instructions and technical backup for the computer program BOXCAR. BOXCAR is a program for the structural analysis and design of reinforced concrete box sections. It has been written to run on IBM or IBM compatible microcomputers. The input routines are user friendly; only minimal experience with computers is required prior to use. Most parameters can be controlled by the user. Knowledge of structural design codes for culverts is required. BOXCAR completes structural analysis for loads due to box weight, soil weight, internal gravity fluid weight, live loads and user specified surcharge loads. Forces resulting from each load condition may be printed out separately. Structural design is in accordance with AASHTO. Design criteria include ultimate flexure, diagonal tension, service load crack control, and service load fatigue. The quantity of output is controlled by the user. All output is formatted for 8.5 by 11 inch paper. User instructions include descriptions of all input variables and design examples. The programmer manual includes listings of all technical subroutines

Chapters 5 to 10

This manual provides guidelines to structural engineers designing plastics and reinforced plastics structural components. It discusses applications commonly used in building construction, transportation structures and vehicles, process industries, sanitary facilities, and marine vessels and structures. The volume of devotes space to the fundamentals of elastic response of structures and provides quantitative methods for analysis and design of plates, beams and axial stressed members, flat sandwich structures, and thin rings and shells fabricated from plastic materials. It also reviews the tests and standards used for evaluating structural plastics fire resistance. Tables, diagrams and chapter notes and references are supplied

A conversation about implicit bias

Explicit bias reflects our perceptions at a conscious level. In contrast, implicit bias is unintentional and operates at a level below our conscious awareness. Implicit stereotypes shaping implicit biases are widely studied in criminal justice, medicine, CEO selection at Fortune 500 companies, etc. However, the problem of unconscious bias remains. E.g., while women constitute an increasing proportion of all STEM undergraduates, they still make up only a small proportion of faculty members at research universities, and they are substantially under-represented in organizational leadership and as recipients of professional awards and prizes. Can we afford to have unintentional perceptions continue to hinder the success and advancement of women and other underrepresented groups? Can we afford to continue to underuse human capital in science? This session at the 2015 Joint Statistical Meetings (JSM) aimed to illuminate what statisticians need to know and do to break the glass ceiling of implicit bias

ESTIMATORS OF BINARY SPATIAL AUTOREGRESSIVE MODELS: A MONTE CARLO STUDY

The goal of this paper is to provide a cohesive description and a critical comparison of the main estimators proposed in the literature for spatial binary choice models. The properties of such estimators are investigated using a theoretical and simulation study, followed by an empirical application. To the authors' knowledge, this is the first paper that provides a comprehensive Monte Carlo study of the estimators' properties. This simulation study shows that the Gibbs estimator performs best for low spatial autocorrelation, while the recursive importance sampler performs best for high spatial autocorrelation. The same results are obtained by increasing the sample size. Finally, the linearized general method of moments estimator is the fastest algorithm that provides accurate estimates for low spatial autocorrelation and large sample size