Applied Statistics Commons^™

Strategies For Reducing Control Group Size In Experiments Using Live Animals, Matthew Kramer, Enrique Font

Conference on Applied Statistics in Agriculture

Reducing the number of animal subjects used in biomedical experiments is desirable for both ethical and practical reasons. Previous suggestions for reducing sample sizes in these experiments have focused on improving experimental designs and methods of statistical analysis; reducing the number of controls (thus, the number of overall animals used) is rarely mentioned. We discuss how the number of current control animals can be reduced, without loss of statistical power, by incorporating information from historical controls, i.e. animals used as controls in similar previous experiments. Using example data from the literature, we describe how to incorporate information from historical controls …

Alternative Estimation Techniques For Correlated Discrete Data, William J. Price Ph.D., Bahman Shafii Ph.D.

Conference on Applied Statistics in Agriculture

Binary or multinomial data often occur in agricultural and biological research. Advancements in measurement and video technologies now allow such data to be sequentially recorded through time or space. These data sets, however, can exhibit a serial correlation structure, which in turn, can bias and influence point estimates as well as inferences made regarding the data. Statistical methods using generalized mixed models and probability distributions such as the beta-binomial and correlated binomial have been proposed as potential solutions for estimating the parameters of interest in these cases. In this paper, we will explore the properties of these techniques through simulation …

Developing Prediction Equations For Fat Free Lean In The Presence Of An Unknown Amount Of Proportional Measurement Error, Zachary J. Hass, Bruce A. Craig, Allan Schinckel

Conference on Applied Statistics in Agriculture

Published prediction equations for fat-free lean mass are widely used by producers for carcass evaluation. These regression equations are commonly derived under the assumption that the predictors are measured without error. In practice, however, it is known that some predictors, such as backfat and loin muscle depth, are measured imperfectly with variance that is proportional to the mean. Failure to account for these measurement errors will cause bias in the estimated equation. In this paper, we describe an empirical Bayes approach, using technical replicates, to accurately estimate the regression relationship in the presence of proportional measurement error. We demonstrate, via …

Paired Competition Analysis Using Mixed Models, Patrick Gallagher, Bruce A. Craig, Tim Luttermoser, Grzegorz Buczkowski

Conference on Applied Statistics in Agriculture

Urban and rural colonies of odorous house ants (Tapinoma sessile) have very different social structures. Urban colonies are very large with hundreds of cohabiting queens, while rural colonies are small with only one queen. To investigate whether worker ant aggressiveness varies across these two colony types, an experiment was performed using an aggression assay, in which 50 ants from each of two colonies were placed in a petri dish and allowed to fight. The response was the total number of dead ants within 24 hours. Because the ants were all the same species and not marked by colony, …

Irrigated And Rainfed Crops Zea Mays L. (Maize) And Glycine Max (Soybean) Acting As A Source Or Sink For Atmospheric Warming At Mead, Nebraska, Jane A. Okalebo Dr., Kenneth G. Hubbard, Andy Suyker

Conference on Applied Statistics in Agriculture

Land Use and Land Cover Change (LULCC) influence the climate at a global and local scale. Using long term microclimate data (2002-2009, 2011-2012) from the Carbon Sequestration Project (CSP), Mead, NE, this study examines how crop selection and water management can mitigate heat in the atmosphere. Mitigation of global warming is dependent on the management of crop lands, and the amount and timing of rainfall during the growing season. Rainfed crops were found to heat the passing air. The irrigated maize crop was able to mitigate 20 to 62% of the sensible heat (H) compared to the rainfed maize counterpart, …

Should Blocks Be Fixed Or Random?, Philip Dixon

Conference on Applied Statistics in Agriculture

Many studies include some form of blocking in the study design. Block effects are rarely of intrinsic interest; instead they are included in a model so that that model reflects the study design. I consider the question of how these block effects should be modeled: as fixed effects or as random effects. I discuss the consequences of the choice, including the recovery of inter-block information when available, give a simple example to illustrate the connection between recovery of inter-block information and pooling two estimators of a treatment effect, and give an example where fitting a model with random block effects …

Comparing Linear Mixed Models For Preliminary Yield Trials That Follow Augmented Experimental Designs, Sudha Neupane Adhikari, Jixiang Wu, Melanie Caffe

Conference on Applied Statistics in Agriculture

COMPARING LINEAR MIXED MODELS FOR PRELIMINARY YIELD TRIALS THAT FOLLOW AUGMENTED EXPERIMENTAL DESIGNS

Sudha Neupane Adhikari, Jixiang Wu, and Melanie Caffe-Treml

Agronomy, Horticulture, and Plant Science Department,

South Dakota State University, Brookings, SD 57007

Abstract

Ineffective control of spatial variation when analyzing field trials data may lead to biased conclusions, which in turn could impact selection efficiency in plant breeding programs. In this study, a group of 78 oats breeding lines were evaluated in preliminary yield trials at four locations in South Dakota in 2015. Four linear mixed models (with and without row and column effects) were compared regarding reduction …

A Bayesian Gwas Method Utilizing Haplotype Clusters For A Composite Breed Population, Danielle F. Wilson-Wells, Stephen D. Kachman

Conference on Applied Statistics in Agriculture

Commercial beef cattle are often composites of multiple breeds. Current methods used to produce genomic predictors are based on the underlying assumption of animals being sampled from a homogeneous population. As a result, the predictors can perform poorly when used to predict the relative genetic merit of animals whose breed composition are different. In part, this is due to the changes in linkage disequilibrium between the markers and the quantitative trait loci as we move from one breed to the next. An alternative model based on breed specific haplotype clusters was developed to allow for differences in linkage disequilibrium across …

Editor's Preface And Table Of Contents, Perla Reyes

Conference on Applied Statistics in Agriculture

2016 Conference on Applied Statistics in Agriculture Proceedings

Graphing Effects As Fuzzy Numbers In Meta-Analysis, Christopher G. Thompson

Journal of Modern Applied Statistical Methods

Prior to quantitative analyses, meta-analysts often explore descriptive characteristics of effect sizes. A graphic is proposed that treats effect sizes as fuzzy numbers. This plot can provide meta-analysts with such information such as heterogeneity of effects, precision of estimates, possible clusters, and existence of outliers.

Principal Component Preliminary Test Estimator In The Linear Regression Model, Sivarajah Arumairajan, Pushpakanthie Wijekoon

Journal of Modern Applied Statistical Methods

A Preliminary Test Estimator is introduced based on Principal Component Regression Estimator defined in the linear regression model when the stochastic restrictions are available in addition to the sample information, and when the explanatory variables are multicollinear. It is further developed as a large sample preliminary test estimator by using Wald (WA), Likelihood Ratio (LR), and Lagrangian Multiplier (LM) tests. Stochastic properties of this estimator based on F test as well as WA, LR, and LM tests are derived, and the performance of the estimator is compared using WA, LR, and LM tests with respect to Mean Square Error Matrix …

Analysis And Modeling Of Statistical Properties Of Fmdfb Subband Coefficients, E. Jebamalar Leavline, Sutha Shunmugam

Journal of Modern Applied Statistical Methods

Fast Multiscale Directional Filter Bank (FMDFB) is an image representation scheme used in several image processing applications. The statistical nature of the FMDFB subbands is analyzed, and a mathematical model of FMDFB coefficients is proposed. Experimental results are justified by goodness-of-fit tests.

Jmasm37: Simple Response Surface Methodology Using Rsreg (Sas), Wan Muhamad Amir, Mohamad Shafiq, Kasypi Mokhtar, Nor Azlida Aleng, Hanafi A.Rahim, Zalila Ali

Journal of Modern Applied Statistical Methods

Response surface methodology (RSM) can be used when the response variable, y, is influenced by several variables, x’s. When treatments take the form of quantitative values, then the true relationship between response variables and independent variables might be known. Examples are given in SAS.

Generalized Singular Value Decomposition With Additive Components, Stan Lipovetsky

Journal of Modern Applied Statistical Methods

The singular value decomposition (SVD) technique is extended to incorporate the additive components for approximation of a rectangular matrix by the outer products of vectors. While dual vectors of the regular SVD can be expressed one via linear transformation of the other, the modified SVD corresponds to the general linear transformation with the additive part. The method obtained can be related to the family of principal component and correspondence analyses, and can be reduced to an eigenproblem of a specific transformation of a data matrix. This technique is applied to constructing dual eigenvectors for data visualizing in a two dimensional …

New Procedures Of Estimating Proportion And Sensitivity Using Randomized Response In A Dichotomous Finite Population, Tanveer A. Tarray, Housila P. Singh

Journal of Modern Applied Statistical Methods

The problem of estimating the population proportion possessing a sensitive attribute using simple random sampling with replacement (SRSWR) is advocated. Two new procedures are proposed. The suggested models are more efficient than the Huang (2004) randomized response technique under some realistic conditions. Numerical and graphic illustrations are given.

Application Of Esscher Transformed Laplace Distribution In Microarray Gene Expression Data, Shanmugasundaram Devika, Sebastian George, Lakshmanan Jeyaseelan

Journal of Modern Applied Statistical Methods

Microarrays allow the study of the expression profile of hundreds to thousands of genes simultaneously. These expressions could be from treated samples and the healthy controls. The Esscher transformed Laplace distribution is used to fit microarray expression data as compared to Normal and Laplace distributions. The Maximum Likelihood Estimation procedure is used to estimate the parameters of the distribution. R codes are developed to implement the estimation procedure. A simulation study is carried out to test the performance of the algorithm. AIC and BIC criterion are used to compare the distributions. It is shown that the fit of the Esscher …

Variable Selection In Regression Using Multilayer Feedforward Network, Tejaswi S. Kamble, Dattatraya N. Kashid

Journal of Modern Applied Statistical Methods

The selection of relevant variables in the model is one of the important problems in regression analysis. Recently, a few methods were developed based on a model free approach. A multilayer feedforward neural network model was proposed for developing variable selection in regression. A simulation study and real data were used for evaluating the performance of proposed method in the presence of outliers, and multicollinearity.

The Goldilocks Dilemma: Impacts Of Multicollinearity -- A Comparison Of Simple Linear Regression, Multiple Regression, And Ordered Variable Regression Models, Grayson L. Baird, Stephen L. Bieber

Journal of Modern Applied Statistical Methods

A common consideration concerning the application of multiple linear regression is the lack of independence among predictors (multicollinearity). The main purpose of this article is to introduce an alternative method of regression originally outlined by Woolf (1951), which completely eliminates the relatedness between the predictors in a multiple predictor setting.

Symmetric Variants Of Logistic Smooth Transition Autoregressive Models: Monte Carlo Evidences, Olaoluwa S. Yaya, Olanrewaju I. Shittu

Journal of Modern Applied Statistical Methods

The Smooth Transition Autoregressive (STAR) models are becoming popular in modeling economic and financial time series. The asymmetric type of the model is the Logistic STAR (LSTAR) model, which is limited in its applications as a result of its asymmetric property, which makes it suitable for modelling specific macroeconomic time series. This study was designed to develop the Absolute Error LSTAR (AELSTAR) and Quadratic LSTAR (QLSTAR) models for improving symmetry and performance in terms of model fitness. Modified Teräsvirta’s Procedure (TP) and Escribano and Jordá's Procedure (EJP) were used to test for nonlinearity in the series. The performance of the …

Liu-Type Logistic Estimators With Optimal Shrinkage Parameter, Yasin Asar

Journal of Modern Applied Statistical Methods

Multicollinearity in logistic regression affects the variance of the maximum likelihood estimator negatively. In this study, Liu-type estimators are used to reduce the variance and overcome the multicollinearity by applying some existing ridge regression estimators to the case of logistic regression model. A Monte Carlo simulation is given to evaluate the performances of these estimators when the optimal shrinkage parameter is used in the Liu-type estimators, along with an application of real case data.