Open Access. Powered by Scholars. Published by Universities.®
Physical Sciences and Mathematics Commons™
Open Access. Powered by Scholars. Published by Universities.®
- Keyword
-
- ANOVA model (1)
- Cell cycle (1)
- Clustering (1)
- Companion animal (1)
- Cotton fiber quality; cotton fiber variability; spatial variability (1)
-
- Data transformation (1)
- Dynamic (1)
- Epigenetics (1)
- Epigenomics (1)
- Experimental design (1)
- Experimental unit (1)
- Gene expression (1)
- Geostatistics (1)
- Hidden Markov model (1)
- Histone modification (1)
- Methylation (1)
- Microarray (1)
- Multi-species (1)
- Multiple testing (1)
- Multivariate likelihood (1)
- Normalization (1)
- Preprocessing (1)
- QTL mapping; multiple traits; SEM; least squares (1)
- Sample size determination (1)
- Spatial clustering (1)
- Spherical covariance (1)
- Statistical model (1)
- Tiling array (1)
Articles 1 - 12 of 12
Full-Text Articles in Physical Sciences and Mathematics
Developing Accurate Spatial Maps Of Cotton Fiber Quality Parameters, Gretchen F. Sassenrath
Developing Accurate Spatial Maps Of Cotton Fiber Quality Parameters, Gretchen F. Sassenrath
Conference on Applied Statistics in Agriculture
Awareness of the importance of cotton fiber quality (Gossypium, L. sps.) has increased as advances in spinning technology require better quality cotton fiber. Recent advances in geospatial information sciences allow an improved ability to study the extent and causes of spatial variability in fiber parameters. However, these studies are often harvested by hand and ginned on small research gins. Fiber quality from cotton lint harvested and ginned in this manner is different from that machine-harvested and ginned on production-scale equipment. The objective of this study was to develop a method of correcting for error introduced into cotton fiber quality parameters …
Nonlinear Mixed Models To Evaluate Effects Of Environment Conditions, Hair Coat, And Anchor Length On Body Temperature During Afternoon Milking Of Holstein Cows In Hawaii, N Aitha, A. M. Parkhurst, C. N. Lee, P. E. Hillman
Nonlinear Mixed Models To Evaluate Effects Of Environment Conditions, Hair Coat, And Anchor Length On Body Temperature During Afternoon Milking Of Holstein Cows In Hawaii, N Aitha, A. M. Parkhurst, C. N. Lee, P. E. Hillman
Conference on Applied Statistics in Agriculture
A hot thermal environment is one factor that causes loss of activity, productivity and even death in cows. One reason may be the effect of hair coat (black or white) on body temperature during different activities and environment conditions. In this study, we attempt to characterize the relationship between body temperature over time using activity, physiological and environmental effects with a nonlinear regression model. The fixed and mixed versions of models are examined. We also examine the effect of the measuring device, anchor length (long or short). Environmental effects, such as, air temperature, thermal heat index (THI), relative humidity, solar …
A Two-Stage Approach For Estimating The Effect Of Dna Methylation On Differential Expression Using Tiling Array Technology, Suk-Young Yoo, R. W. Doerge
A Two-Stage Approach For Estimating The Effect Of Dna Methylation On Differential Expression Using Tiling Array Technology, Suk-Young Yoo, R. W. Doerge
Conference on Applied Statistics in Agriculture
Epigenetics is the study of heritable alterations in gene function without changing the DNA sequence itself. It is known that epigenetic modifications such as DNA methylation and histone modifications are highly correlated with the regulation of gene expression. A twostage analysis is proposed that employs a hidden Markov model and a linear model to evaluate differential expression as related to DNA methylation for the purpose of examining the effects of DNA methylation on gene regulation using tiling array technology. In the first stage, a hidden Markov model (HMM) is employed to estimate the methylation status per tile by utilizing information …
Spatial Clustering Using The Likelihood Function, April Kerby, David Marx, Ashok Samal, Viacheslav Adamchuk
Spatial Clustering Using The Likelihood Function, April Kerby, David Marx, Ashok Samal, Viacheslav Adamchuk
Conference on Applied Statistics in Agriculture
Clustering has been widely used as a tool to group multivariate observations that have similar characteristics. However, most attempts at formulating a method to group similar multivariate observations while taking into account their spatial location are relatively ad hoc and do not account for the underlying spatial structure of the variables measured [12, 13, 14]. This paper proposes a method to spatially cluster similar observations based on the likelihood function. The geographic or spatial location of the observations can be incorporated into the likelihood of the multivariate normal distribution through the variance-covariance matrix. The variance-covariance matrix can be computed using …
Statistical Issues In Efficacy Evaluation For Companion Animal Drug Development, Zhanglin Lin Cui, Wherly Hoffman
Statistical Issues In Efficacy Evaluation For Companion Animal Drug Development, Zhanglin Lin Cui, Wherly Hoffman
Conference on Applied Statistics in Agriculture
Companion animals, commonly called pets, are animals such as dogs, cats, and horses. The companion animal drug market has expanded rapidly in recent years. Two major points of focus in companion animal drug development are therapeutics and parasiticides. From a statistics point of view, experimental design, experimental unit determination, sample size estimation and reestimation, treatment design, data transformation, multiple testing, and proper modeling are major statistical issues when efficacy evaluation in a companion animal study is conducted. These major statistical issues are addressed using two clinical studies as examples: Reconcile® (Fluoxetine) for the treatment of separation anxiety in dogs and …
Modeling Seasonal Wine Grape Development Using A Mixture Technique, William J. Price, Bahman Shafii, Paul E. Blom, Julie M. Tarara, Nick Dokoozlian, Luis J. Sanchez
Modeling Seasonal Wine Grape Development Using A Mixture Technique, William J. Price, Bahman Shafii, Paul E. Blom, Julie M. Tarara, Nick Dokoozlian, Luis J. Sanchez
Conference on Applied Statistics in Agriculture
Biological growth data typically display an increasing sigmoidal pattern over time. Grape development is no exception and shows a similar general trend. A detailed examination of the growth process in grapes, however, reveals a few systematic deviations from this pattern. Specifically, grape development is often characterized by localized areas of growth plateaus leading to an overall growth pattern referred to as a double sigmoidal curve. Capturing and characterizing these local changes in growth is important as they represent important phases in grape development such as veraison. This paper utilizes a model adapted from the technique of mixture models to estimate …
Comparisons Of Two Symmetric Density Function Solutions Of Aphid Population Growth Models, J. H. Matis, T. R. Kiffey, W. Van Der Werf, A. C. Costamagna, T. I. Matis, G. J. Michels Jr
Comparisons Of Two Symmetric Density Function Solutions Of Aphid Population Growth Models, J. H. Matis, T. R. Kiffey, W. Van Der Werf, A. C. Costamagna, T. I. Matis, G. J. Michels Jr
Conference on Applied Statistics in Agriculture
Aphids are among the world's most devastating crop pests, and their population trajectories in field crops are characterized by rapid boom and bust, under the influence of bottom up (host plant) and top down (natural enemy) forces. Theoretical development in aphid growth trajectory modeling has recently advanced quite significantly, and the logistic and normal probability density functions have been found to provide analytical solutions to mechanistic models of the aphid population growth dynamics. The logistic or hyperbolic secant squared model captures a growth trajectory shaped by negative feedback of the aphid population on itself, due to the accumulation of adverse …
Statistical Issues In The Normalizationof Multi-Species Microarray Data, John R. Stevens, Balasubramanian Ganesan, Prerak Desai, Sweta Rajan, Bart C. Weimer
Statistical Issues In The Normalizationof Multi-Species Microarray Data, John R. Stevens, Balasubramanian Ganesan, Prerak Desai, Sweta Rajan, Bart C. Weimer
Conference on Applied Statistics in Agriculture
Several species of bacteria are involved in the production of cheese, including Lactobacillus brevis and Lactococcus lactis. A custom-designed Affymetrix microarray was recently developed to study gene expression in three organisms on a single chip. This array contains only perfect match features for the coding and non-coding regions in the genomes of all three sequences. The multi-species nature of this array version raises interesting questions regarding the preprocessing or normalization strategies for the analysis of gene expression data. We present and evaluate several possible strategies using both cDNA dilution data and experimental expression data from a repeated measures design. The …
Multi-Trait Qtl Mapping Using A Structural Equation Model, Xiaojuan Mi, Kent M. Eskridge, Dong Wang
Multi-Trait Qtl Mapping Using A Structural Equation Model, Xiaojuan Mi, Kent M. Eskridge, Dong Wang
Conference on Applied Statistics in Agriculture
Research on mapping quantitative trait loci (QTL) often results in data on a number of traits that have well established causal relationships. Many multi-trait QTL mapping methods, taking into account the correlation among the multiple traits, have been developed to improve the statistical power of the test for QTL and the precision of parameter estimation. However none of these methods are capable of incorporating the causal structure among the traits with the consequence that genetic functions of the QTL may not be fully understood. Structural equation modeling (SEM) allows researchers to explicitly characterize the causal structure among the variables and …
Dynamic Clustering Of Cell-Cycle Microarray Data, Lingling An, R. W. Doerge
Dynamic Clustering Of Cell-Cycle Microarray Data, Lingling An, R. W. Doerge
Conference on Applied Statistics in Agriculture
The cell cycle is a crucial series of events that are repeated over time, allowing the cell to grow, duplicate, and split. Cell-cycle systems play an important role in cancer and other biological processes. Using gene expression data gained from microarray technology it is possible to group or cluster genes that are involved in the cell-cycle for the purpose of exploring their functional co-regulation. Typically, the goal of clustering methods as applied to gene expression data is to place genes with similar expression patterns or profiles into the same group or cluster for the purpose of inferring the function of …
Testing Variance Components By Two Jackknife Methods, Jixiang Wu, Johnie N. Jenkins, Jack C. Mccarty
Testing Variance Components By Two Jackknife Methods, Jixiang Wu, Johnie N. Jenkins, Jack C. Mccarty
Conference on Applied Statistics in Agriculture
The jackknife method, a resampling technique, has been widely used for statistical tests for years. The pseudo value based jackknife method (defined as pseudo jackknife method) is commonly used to reduce the bias for an estimate; however, sometimes it could result in large variation for an estimate and thus reduce the power for parameters of interest. In this study, a non-pseudo value based jackknife method (defined as non-pseudo jackknife method) was used for testing variance components under mixed linear models. We compared this non-pseudo value based jackknife method and the pseudo value based method by simulation regarding their biases, Type …
Editor's Preface And Table Of Contents, Gary L. Gadbury
Editor's Preface And Table Of Contents, Gary L. Gadbury
Conference on Applied Statistics in Agriculture
These proceedings contain papers presented in the twentieth annual Kansas State University Conference on Applied Statistics in Agriculture, held in Manhattan, Kansas, April 27- April 29, 2008.