Regression Models For Mixed Over-Dispersed Poisson And Continuous Clustered Data: Modeling Bmi And Number Of Cigarettes Smoked Per Day,
2012
Brigham and Women's Hospital, Boston, MA
Regression Models For Mixed Over-Dispersed Poisson And Continuous Clustered Data: Modeling Bmi And Number Of Cigarettes Smoked Per Day, Folefac Atem, Julius S. Ngwa, Abidemi Adeniji
Journal of Modern Applied Statistical Methods
Clustered data, multiple observations collected on the same experimental unit, is common in epidemiological studies. Bivariate outcome data is often the result of interest in two correlated response variables. An efficient method is presented for dealing with bivariate outcomes when one outcome is continuous and the other is a count using a simple transformation to handle over-dispersed Poisson data. A multilevel analysis was performed on data from the National Health Interview Survey (NHIS) with body mass index (BMI) and the number of cigarettes smoked per day (NCS) as responses. Results show that these random effects models yield misleading results in …
A Study On Underwriting Cycle Of Property Insurance Industry Of China,
2012
College of Hunan University, Changsha Hunan Province, China
A Study On Underwriting Cycle Of Property Insurance Industry Of China, Lin Zhang, Linjuan Tang
Journal of Modern Applied Statistical Methods
Methods in underwriting cycle research are compared. A second-order autoregressive model, which includes structural transition and Christiano-Fitzgerald (CF) Filter method, is used to analyze China’s underwriting cycle with annual property insurance loss ratio data from 1982 to 2008. Results show that the underwriting cycle is 11-12 years and, from the phase of underwriting cycle, management suggestions about underwriting cycle phenomenon are provided.
Ratio Type Estimator Of Ratio Of Two Population Means In Stratified Random Sampling,
2012
Vikram University, Ujjain, M.P., India
Ratio Type Estimator Of Ratio Of Two Population Means In Stratified Random Sampling, Rajesh Tailor, Sunil Chouhan
Journal of Modern Applied Statistical Methods
A ratio estimator is proposed for the ratio of two population means using auxiliary information in stratified random sampling. Bias and mean squared error expressions are obtained under large sample approximation, and the proposed estimator is compared both theoretically and empirically with the conventional estimator of ratio for two population means in stratified random sampling.
Translation Representations And Scattering By Two Intervals,
2012
Wright State University - Main Campus
Translation Representations And Scattering By Two Intervals, Palle Jorgensen, Steen Pedersen, Feng Tian
Mathematics and Statistics Faculty Publications
Studying unitary one-parameter groups in Hilbert space (U(t), H), we show that a model for obstacle scattering can be built, up to unitary equivalence, with the use of translation representations for L2-functions in the complement of two finite and disjoint intervals. The model encompasses a family of systems (U(t), H). For each, we obtain a detailed spectral representation, and we compute the scattering operator and scattering matrix. We illustrate our results in the Lax-Phillips model where (U(t), H) represents an acoustic wave equation …
The Impact Of Violating Factor Scaling Method Assumptions On Latent Mean Difference Testing In Structured Means Models,
2012
The University of Texas at Austin
The Impact Of Violating Factor Scaling Method Assumptions On Latent Mean Difference Testing In Structured Means Models, Dandan Wang, Tiffany A. Whittaker, S. Natasha Beretvas
Journal of Modern Applied Statistical Methods
Type I error rates and power of the likelihood ratio test and bias of the standardized effect size measure associated with the latent mean difference in structured means modeling are examined when violating the assumptions underlying the two available factor scaling methods under various conditions. Implications and recommendations are discussed.
New Approximate Bayesian Confidence Intervals For The Coefficient Of Variation Of A Gaussian Distribution,
2012
Research Center for Bayesian Applications, Inc., Largo, FL
New Approximate Bayesian Confidence Intervals For The Coefficient Of Variation Of A Gaussian Distribution, Vincent A. R. Camara
Journal of Modern Applied Statistical Methods
Confidence intervals are constructed for the coefficient of variation of a Gaussian distribution. Considering the square error and the Higgins-Tsokos loss functions, approximate Bayesian models are derived and compared to a published classical model. The models are shown to have great coverage accuracy. The classical model does not always yield the best confidence intervals; the proposed models often perform better.
The Weighted Hellinger Distance For Kernel Distribution Estimator Of Function Of Observations,
2012
Jordan University of Science and Technology, Irbid, Jordan
The Weighted Hellinger Distance For Kernel Distribution Estimator Of Function Of Observations, Abdel-Razzaq Mugdadi
Journal of Modern Applied Statistical Methods
The asymptotic mean weighted Hellinger distance (AMWHD) is derived for the kernel distribution estimator of a function of observations. In addition, the AMWHD is compared with the asymptotic mean integrated square error (AMISE) of the estimator. A completely data based method is proposed to select the bandwidth in the estimator using the mean weighted Hellinger distance (MWHD).
A Poisson Regression Model For Female Radium Dial Workers,
2012
Western Illinois University
A Poisson Regression Model For Female Radium Dial Workers, Tze-San Lee
Journal of Modern Applied Statistical Methods
A Poisson regression model with interaction terms was applied to study the dose response relationship for radium-induced skeletal cancers. The model showed that the expected frequency count of bone tumors depended not only on the logarithmic dose and the time since first exposure, but also on the interaction between the logarithmic dose and the time since first exposure, whereas the dose-response model for head tumors depended only on the logarithmic dose.
Jmasm 32: Sas Template For Single-Subject Experimental Designs,
2012
Chungnam National University, Deajeon, Korea
Jmasm 32: Sas Template For Single-Subject Experimental Designs, Hyewon Chung, Jiseon Kim, Ryoungsun Park
Journal of Modern Applied Statistical Methods
Meta-analysis has been used to synthesize research findings and to evaluate the effectiveness of treatments or the accuracy of diagnostic tools. Although meta-analytic techniques were developed to synthesize the results of several studies, controversy exists as to how to quantify the results from singlesubject experimental designs (SSEDs). The most commonly used metrics are reviewed, including nonregression and regression based methods. The application of the SAS template is demonstrated through simulated data sets. The SAS templates can be modified to accommodate a more complex data structure.
Managing Clustered Data Using Hierarchical Linear Modeling,
2012
Utah Valley University
Managing Clustered Data Using Hierarchical Linear Modeling, Russell Warne
Russell T Warne
Researchers in nutrition research often use cluster or multistage sampling to gather participants for their studies. These sampling methods often produce violations of the assumption of data independence that most traditional statistics share. Hierarchical linear modeling is a statistical method that can overcome violations of the independence assumption and lead to correct analysis of data, yet it is rarely used in nutrition research. The purpose of this viewpoint is to illustrate the benefits of hierarchical linear modeling within a nutrition research context.
Differential Patterns Of Interaction And Gaussian Graphical Models,
2012
UCLA, Statistics
Differential Patterns Of Interaction And Gaussian Graphical Models, Masanao Yajima, Donatello Telesca, Yuan Ji, Peter Muller
COBRA Preprint Series
We propose a methodological framework to assess heterogeneous patterns of association amongst components of a random vector expressed as a Gaussian directed acyclic graph. The proposed framework is likely to be useful when primary interest focuses on potential contrasts characterizing the association structure between known subgroups of a given sample. We provide inferential frameworks as well as an efficient computational algorithm to fit such a model and illustrate its validity through a simulation. We apply the model to Reverse Phase Protein Array data on Acute Myeloid Leukemia patients to show the contrast of association structure between refractory patients and relapsed …
Variation Analysis For Fiber Quality Traits Among Different Positionsin Eight Upland Cotton Cultivars,
2012
Kansas State University Libraries
Variation Analysis For Fiber Quality Traits Among Different Positionsin Eight Upland Cotton Cultivars, Yi Xu, Johnie N. Jenkins, Jack C. Mccarty, Jixiang Wu
Conference on Applied Statistics in Agriculture
Equivalencyof fiber quality within a plant of upland cotton, Gossypium hirsutum L., is very important. There are several traits within a plant that can be used to measure fiber quality and five of those traits will be investigated. Eight representative upland cultivars were grown at the Plant Science Research Farm at Mississippi State University in 1986 and five fiber traits: micronaire, fiber elongation, 2.5% and 50% span length, and fiber strength, were measured at different plant locations. The analysis of the study was modeled after a crop stability analysis with plant locations being treated as environments in the analysis. Three …
Towards Better Fdr Procedures For Discrete Test Statistics,
2012
Kansas State University Libraries
Towards Better Fdr Procedures For Discrete Test Statistics, Xiongzhi Chen, R. W. Doerge
Conference on Applied Statistics in Agriculture
The false discovery rate (FDR) has been a widely used error measure in situations where a large number of tests are conducted simultaneously. Most methods that control the FDR at a prespeci ed level, or estimate the FDR of a multiple testing procedure (FDR procedures), were essentially developed for continuous test statistics. As such, their performances need to be carefully assessed when applied to discrete test statistics. We review some popular FDR procedures, point out a key reason for their excessive conservativeness when applied to discrete p-values, and suggest an improvement for these methods for such p-values.
Statistical Considerations When Using Hysteresis To Estimate Internal Heat Load In Dairy Cows,
2012
Kansas State University Libraries
Statistical Considerations When Using Hysteresis To Estimate Internal Heat Load In Dairy Cows, S. Maynes, A. M. Parkhurst
Conference on Applied Statistics in Agriculture
Water is often used to manage heat stress in dairy cattle. Sprinklers are often placed over the feed bunk or used while cattle are waiting to be milked, however in this experiment cattle were given control over water with a cow-activated shower. Previous studies have focused on how wetting can lower body emperature or reduce respiration rates. An alternative way to investigate this management practice is to examine internal heat loads. Internal heat load can be quantified by fitting a hysteresis loop to daily field data. The hysteresis loop is formed by a phase diagram of body temperature versus an …
The Nuances Of Statistically Analyzing Next-Generation Sequencing Data,
2012
Kansas State University Libraries
The Nuances Of Statistically Analyzing Next-Generation Sequencing Data, Sanvesh Srivastava, R. W. Doerge
Conference on Applied Statistics in Agriculture
High-throughput sequencing technologies, in particular next-generation sequencing (NGS) technologies, have emerged as the preferred approach for exploring both gene function and pathway organization. Data from NGS technologies pose new computational and statistical challenges because of their massive size, limited replicate information, large number of genes (high-dimensionality), and discrete form. They are more complex than data from previous high-throughput technologies such as microarrays. In this work we focus on the statistical issues in analyzing and modeling NGS data for selecting genes suitable for further exploration and present a brief review of the relevant statistical methods. We discuss visualization methods to assess …
Treatment Heterogeneity And Potential Outcomes In Linear Mixed Effects Models,
2012
Kansas State University Libraries
Treatment Heterogeneity And Potential Outcomes In Linear Mixed Effects Models, Troy E. Richardson, Gary L. Gadbury
Conference on Applied Statistics in Agriculture
Studies commonly focus on estimating a mean treatment effect in a population. However, in some applications the variability of treatment effects across individual units may help to characterize the overall effect of a treatment across the population. Consider a set of treatments, {T,C}, where T denotes some treatment that might be applied to an experimental unit and C denotes a control. For each of N experimental units, the duplet {rTᵢ,rCᵢ}, i = 1,2, … , N, represents the potential response of the i th experimental unit if treatment were applied and the response …
Identifying Spectra Important For Prediction Of Senescent Grassland Canopy Structure,
2012
Kansas State University Libraries
Identifying Spectra Important For Prediction Of Senescent Grassland Canopy Structure, Rebecca Phillips, Nicanor Saliendra, Mark West
Conference on Applied Statistics in Agriculture
Managers of the nearly 0.5 million ha of public lands in North and South Dakota, USA rely heavily on manual measurements of vegetation properties to ensure conservation of grassland structure for wildlife and forage for livestock. Spectral imaging data may be useful in assessment of large (>100,000 ha) landscapes, as in the Grand River National Grassland (GRNG), South Dakota. Here, we examined the predictive potential for the Advanced High Resolution Spectrometer (AVIRIS) to estimate mixed-grass prairie canopy structural attributes (photosynthetically active vegetation (kg PV ha-1), non-photosynthetically active vegetation (kg NPV ha-1), total standing crop (kg …
Exploration Of Reactant-Product Lipid Pairs In Mutant-Wild Type Lipidomics Experiments,
2012
Kansas State University Libraries
Exploration Of Reactant-Product Lipid Pairs In Mutant-Wild Type Lipidomics Experiments, Lianqing Zheng, Gary L. Gadbury, Jyoti Shah, Ruth Welti
Conference on Applied Statistics in Agriculture
High-throughput metabolite analysis is very important for biologists to identify the functions of genes. A mutation in a gene encoding an enzyme is expected to alter the level of the metabolites which serve as the enzyme’s reactant(s) (also known as substrate) and product(s). To find the function of a mutated gene, metabolite data from a wild-type organism and a mutant are compared and candidate reactants and products are identified. The screening principle is that the concentration of reactants will be higher and the concentration of products will be lower in the mutant than in wild type. This is because the …
Variance Inflation Factors In Regression Models With Dummy Variables,
2012
Kansas State University Libraries
Variance Inflation Factors In Regression Models With Dummy Variables, Leigh Murray, Hien Nguyen, Yu-Feng Lee, Marta D. Remmenga, David W. Smith
Conference on Applied Statistics in Agriculture
Variance Inflation Factors (VIFs) are used to detect collinearity among predictors in regression models. Textbook explanation of collinearity and diagnostics such as VIFs have focused on numeric predictors as being "co-linear" or "co-planar", with little attention paid to VIFs when a dummy variable is included in the model. This work was motivated by two regression models with high VIFs, where "standard' interpretations of causes of collinearity made no sense. The first was an alfalfa-breeding model with two numeric predictors and two dummy variables. The second was an economic model with one numeric predictor, one dummy and the numeric x dummy …
Stability Analysis For Yield And Seed Quality Of Soybean [Glycine Max (L.) Merril] Across Different Environments In Eastern South Dakota,
2012
Kansas State University Libraries
Stability Analysis For Yield And Seed Quality Of Soybean [Glycine Max (L.) Merril] Across Different Environments In Eastern South Dakota, Kaushal Raj Chaudhary, Jixiang Wu
Conference on Applied Statistics in Agriculture
Genotype-environment interaction has always been an important and challenging issue for plant breeders in developing desirable varieties. Determination of genotype and environment is common in breeding program as it helps to find out the genotypes that have wide or specific adaptability across various environmental conditions. In this study, fifteen varieties of soybean were evaluated for stability of grain yield (ton/ha), protein content (%), and oil content (%) at six different locations of Eastern South Dakota in 2011. Mixed linear model and Additive main effects and multiplicative interactions (AMMI) were applied to detect genotype-by-environment (G*E) interactions and stability of each variety …
