Statistics and Probability Commons^™

Longitudinal Functional Models With Structured Penalties, Madan G. Kundu, Jaroslaw Harezlak, Timothy W. Randolph

Johns Hopkins University, Dept. of Biostatistics Working Papers

Collection of functional data is becoming increasingly common including longitudinal observations in many studies. For example, we use magnetic resonance (MR) spectra collected over a period of time from late stage HIV patients. MR spectroscopy (MRS) produces a spectrum which is a mixture of metabolite spectra, instrument noise and baseline profile. Analysis of such data typically proceeds in two separate steps: feature extraction and regression modeling. In contrast, a recently-proposed approach, called partially empirical eigenvectors for regression (PEER) (Randolph, Harezlak and Feng, 2012), for functional linear models incorporates a priori knowledge via a scientifically-informed penalty operator in the regression function …

Selection Of Mixed Sampling Plan With Qss-1(N; CN, CT) Plan As Attribute Plan Indexed Through Mapd And Lql, R. Sampath Kumar, M. Indra, R. Radhakrishnan

Journal of Modern Applied Statistical Methods

A procedure for the construction and selection of the mixed sampling plan using MAPD as a quality standard with the QSS-1 (n; c_N, c_T) plan as an attribute plan is presented. The plans indexed through MAPD and LQL are constructed and compared for efficiency. Tables are provided for selection of an appropriate sampling plan.

On Some Negative Integer Moments Of Quasi-Negative-Binomial Distribution, Anwar Hassan, Sheikh Bilal

Journal of Modern Applied Statistical Methods

Negative integer moments of the quasi-negative-binomial distribution (QNBD) are investigated. This distribution includes recurrence relations which are helpful in the solution of many applied statistical problems, particularly in life testing and survey sampling, where ratio estimators are useful. Results study show the negative-binomial distribution when the parameter θ₂ is zero and also indicate the mean of the QNBD model when its parameters are changed.

On Some Properties And Estimation Of Size-Biased Polya-Eggenberger Distribution, Anwar Hassan, Sheikh Bilal, Imtiyaz Ahmad Shah

Journal of Modern Applied Statistical Methods

A size-biased version of Polya-Eggenberger distribution is introduced explicitly and by a mixture model. The proposed distribution is unimodal with positive integer moments. The recurrence relation between moments (about the origin) of the proposed distribution is established and its relationship with other distributions is discussed. Different estimation techniques are proposed to estimate the parameters of the distribution.

Regression Split By Levels Of The Dependent Variable, Stan Lipovetsky

Journal of Modern Applied Statistical Methods

Multiple regression coefficients split by the levels of the dependent variable are examined. The decomposition of the coefficients can be defined by points on the ordinal scale or by levels in the numerical response using the Gifi system of binary variables. This approach permits consideration of specific values of the coefficients at each layer of the response variable. Numerical results illustrate how to identify levels of interpretable regression coefficients.

Extreme Value Charts And Analysis Of Means (Anom) Based On The Log Logistic Distribution, B. Srinivasa Rao, J. Pratapa Reddy, G. Sarath Babu

Journal of Modern Applied Statistical Methods

A probability model of a quality characteristic is assumed to follow a log logistic distribution. This article proposes variable control charts, termed extreme value charts, based on the extreme values of each subgroup. The control chart constants depend on the probability model of the extreme order statistics and the size of each subgroup. The analysis of means (ANOM) technique for a skewed population is applied with respect to log logistic distribution. Results are illustrated using examples based on real data.

Examining Growth With Statistical Shape Analysis And Comparison Of Growth Models, Deniz Sigirli, Ilker Ercan

Journal of Modern Applied Statistical Methods

Growth curves have been widely used in the fields of biology, zoology and medicine for assessing some measurable trait of an organism, such as height, weight, area or volume. In statistical shape analysis, a size measure is obtained using the geometrical information of an object as opposed to linear measurements. The performances of commonly used non-linear growth curves are compared by using centroid size as a size measure in a simulation study. An example is provided on the relationship between centroid size of the cerebellum and disease duration in multiple sclerosis patients.

Examining Multiple Comparison Procedures According To Error Rate, Power Type And False Discovery Rate, Guven Ozkaya, Ilker Ercan

Journal of Modern Applied Statistical Methods

Examining pairwise differences between means is a common practice of applied researchers, and the selection of an appropriate multiple comparison procedure (MCP) is important for analyzing pairwise comparisons. This study examines the performance of MCPs under the assumption of homogeneity of variances for various numbers of groups with equal and unequal sample sizes via a simulation study. MCPs are compared according to type I error rate, power type and false discovery rate (FDR). Results show that the LSD and Duncan procedures have high error rates and Scheffe’s procedure has low power; no remarkable differences between the other procedures considered were …

Class(Es) Of Factor-Type Estimator(S) In Presence Of Measurement Error, Diwakar Shukla, Sharad Pathak, Narendra Singh Thakur

Journal of Modern Applied Statistical Methods

When data is collected via sample survey it is assumed whatever is reported by a respondent is correct. However, given the issues of prestige bias, personal respect and honor, respondents’ self-reported data often produces over- or under- estimated values as opposed to true values regarding the variables under question. This causes measurement error to be present in sample values. This article considers the factortype estimator as an estimation tool and examines its performance under a measurement error model. Expressions of optimization are derived and theoretical results are supported by numerical examples.

Exact Logistic Regression For A Matched Pairs Case-Control Design With Polytomous Exposure Variables, Shyam S. Ganguly

Journal of Modern Applied Statistical Methods

Logistic regression methods are useful in estimating odds ratios under matched pairs case-control designs when the exposure variable of interest is binary or polytomous in nature. Analysis is typically performed using large sample approximation techniques. When conducting the analysis with polytomous exposure variable, situations where the numbers of discordant pairs in the resulting cells are small or the data structure is sparse can be encountered. In such situations, the asymptotic method of analysis is questionable, thus an exact method of analysis may be more suitable. A method is presented that performs exact inference in the case of pair-wise matched case-control …

Modified Edf Goodness Of Fit Tests For Logistic Distribution Under Srs And Rss, S. A. Al-Subh, M. T. Alodat, Kamaruzaman Ibrahim, Abdul Aziz Jemain

Journal of Modern Applied Statistical Methods

Modified forms of goodness of fit tests are presented for the logistic distribution using statistics based on the empirical distribution function (EDF). A method to improve the power of the modified EDF goodness of fit tests is introduced based on Ranked Set sampling (RSS). Data are collected via the Ranked Set Sampling (RSS) technique (McIntyre, 1952). Critical values for the logistic distribution with unknown parameters are provided and the powers of the tests are given for a number of alternative distributions. A simulation study is presented to illustrate the power of the new method.

Small-To-Medium Enterprises And Economic Growth: A Comparative Study Of Clustering Techniques, Karim K. Mardaneh

Journal of Modern Applied Statistical Methods

Small-to-medium enterprises (SMEs) in regional (non-metropolitan) areas are considered when economic planning may require large data sets and sophisticated clustering techniques. The economic growth of regional areas was investigated using four clustering algorithms. Empirical analysis demonstrated that the modified global k-means algorithm outperformed other algorithms.

Posterior Estimates Of Poisson Distribution Using R Software, Raja Sultan, S.P. Ahmad

Journal of Modern Applied Statistical Methods

The Bayesian estimation of unknown parameter of the Poisson distribution is examined under different priors. The posterior distributions for the unknown parameter of the Poisson distribution are derived using the following priors: uniform, Jeffrey’s, Gamma distribution, Gamma-Chi-square distribution, Gammaexponential distribution and Chi-square-exponential distribution. Numerical and graphical illustrations of the posterior densities of the parameters of interest were conducted using R Software.

Weighted Cook-Johnson Copula And Their Characterizations: Application To Probably Modeling Of The Hot Spring Eruptions, Hakim Bekrizadeh, Gholam Ali Parham, Mohamd Reza Zadkarmi

Journal of Modern Applied Statistical Methods

Copulas have emerged as a practical method for multivariate modeling. A limited amount of work has been conducted regarding the application of copula-based modeling in context analysis. This study generalizes the Cook-Johnson copula under the appropriate weighted function and provides examples and the properties of the generalized Cook-Johnson copula. Results show that the generalized Cook-Johnson copula is suitable for probable modeling of hot spring eruption.

Comparing Two Independent Groups Via A Quantile Generalization Of The Wilcoxon-Mann-Whitney Test, Rand R. Wilcox

Journal of Modern Applied Statistical Methods

The Wilcoxon-Mann-Whitney test, as well as modern improvements, are based in part on an estimate of p = P(D < 0), where D = X−Y and X and Y are independent random variables; a common goal is to test H₀: p = 0.5. This corresponds to testing H₀: ξ_0.5, where ξ_0.5 is the 0.5 quantile of the distribution of D. If the distributions associated with X and Y do not differ, then D has a symmetric distribution about zero. In particular, ξ_q + ξ_1-q = 0 for any q ≤ 0.5, where ξ_q is the q^th quantile. Methods aimed at testing H₀: p = 0.5 are generalized by …

Single Sampling Plans For Variables Indexed By Aql And Aoql With Measurement Error, R. Sankle, J.R. Singh

Journal of Modern Applied Statistical Methods

Single sampling plans are investigated for variables indexed by acceptable quality level (AQL) and average outgoing quality limit (AOQL) under measurement error. Procedures and tables are provided for selection of single sampling plans for variables for given AQL and AOQL when rejected lots are 100% inspected for replacement of a nonconforming unit. For a particular sampling plan in operation for an observed measurement, a method for determining true operating characteristic (OC) functions and average outgoing quality (AOQ) is described for various error sizes.

An Extension Of Cochran-Orcutt Procedure For Generalized Linear Regression Models With Periodically Correlated Errors, Abdullah A. Smadi, Nour H. Abu-Afouna

Journal of Modern Applied Statistical Methods

An important assumption of ordinary regression models is independence among errors. This research considers the case of periodically correlated errors following the periodic AR model of order 1 (PAR(1)). The remedial measure for correlated errors in regression known as the Cochran-Orcutt procedure is generalized to the case of periodically correlated errors. The motivation for making such generalizations is that the response data may inhibit some seasonality, which may not be captured by the traditional AR(1) autoregressive model. The proposed procedure is described and the bias and MSE of the resulting intercept and slope parameter estimates of the simple LR model …

A Proposed Ridge Parameter To Improve The Least Square Estimator, Ghadban Khalaf

Journal of Modern Applied Statistical Methods

Ridge regression, a form of biased linear estimation, is a more appropriate technique than ordinary least squares (OLS) estimation in the case of highly intercorrelated explanatory variables in the linear regression model Y = β + u. Two proposed ridge regression parameters from the mean square error (MSE) perspective are evaluated. A simulation study was conducted to demonstrate the performance of the proposed estimators compared to the OLS, HK and HKB estimators. Results show that the suggested estimators outperform the OLS and the other estimators regarding the ridge parameters in all situations examined.

Graphical Modeling For High Dimensional Data, Munni Begum, Jay Bagga, C. Ann Blakey

Journal of Modern Applied Statistical Methods

With advances in science and information technologies, many scientific fields are able to meet the challenges of managing and analyzing high-dimensional data. A so-called large p small n problem arises when the number of experimental units, n, is equal to or smaller than the number of features, p. A methodology based on probability and graph theory, termed graphical models, is applied to study the structure and inference of such high-dimensional data.

A Graphical Examination Of Variable Deletion Within The Mewma Statistic, Jay R. Schaffer, Shawn Vandenhul

Journal of Modern Applied Statistical Methods

A general procedure for identifying the variable(s) that contribute(s) to the signal of the multivariate extension of the exponentially weighted moving average (MEWMA) chart is presented. The procedure systematically removes one or two variables from the MEWMA statistic calculations. Percentages are calculated for correctly identifying various shifts.

Testing The Population Coefficient Of Variation, Shipra Banik, B. M. Golam Kibria, Dinesh Sharma

Journal of Modern Applied Statistical Methods

The coefficient of variation (CV), which is used in many scientific areas, measures the variability of a population relative to its mean and standard deviation. Several methods exist for testing the population CV. This article compares a proposed bootstrap method to existing methods. A simulation study was conducted under both symmetric and skewed distributions to compare the performance of test statistics with respect to empirical size and power. Results indicate that some of the proposed methods are useful and can be recommended to practitioners.

Bayesian Estimation Of Erlang Distribution Under Different Generalized Truncated Distributions As Priors, Adil H. Khan, T.R. Jan

Journal of Modern Applied Statistical Methods

Various generalized truncated distributions are considered as independent informative priors for estimating shape and scale parameters of the Erlang distribution. In addition, various special cases are also discussed.

Multivariate Generalized Poisson Distribution For Interference On Selected Non-Communicable Diseases In Lagos State, Nigeria, Adewara Johnson Ademola, Mbata Ugochuckwu Ahamefula

Journal of Modern Applied Statistical Methods

Multivariate Generalized Poisson Distribution (MGPD) models are applied to make inferences regarding non-communicable diseases, diabetes, hypertension, stroke and ulcer in Lagos State, Nigeria. The generalized Poisson distribution is employed due to its usefulness in modeling count data in the presence of either over- or under- dispersion. Results show that the correlation between ulcer and stroke is not significant. Other pairwise comparisons of diseases are significant, thus implying that a patient who suffers from diabetes or stroke has a high propensity to also be hypertensive.

Ferrieri's Index Of Openness Applied To Remittances To Developing Countries, Gaetano Ferrieri

Journal of Modern Applied Statistical Methods

A new methodology to measure international openness and globalization is described. This allows capacity to be effectively combined with size in a number of socio-economic areas, such as trade, migration and foreign investment. The method is applied to remittances to developing countries.

Group Testing Regression Models, Boan Zhang

Department of Statistics: Dissertations, Theses, and Student Research

Group testing, where groups of individual specimens are composited to test for the presence or absence of a disease (or some other binary characteristic), is a procedure commonly used to reduce the costs of screening a large number of individuals. Statistical research in group testing has traditionally focused on a homogeneous population, where individuals are assumed to have the same probability of having a disease. However, individuals often have different risks of positivity, so recent research has examined regression models that allow for heterogeneity among individuals within the population. This dissertation focuses on two problems involving group testing regression models. …

G-Cimp Status Prediction Of Glioblastoma Samples Using Mrna Expression Data, Mehmet Baysan, Serdar Bozdag, Margaret C. Cam, Svetlana Kotliarova, Susie Ahn, Jennifer Walling, Jonathan K. Killian, Holly Stevenson, Paul Meltzer, Howard A. Fine

Mathematics, Statistics and Computer Science Faculty Research and Publications

Glioblastoma Multiforme (GBM) is a tumor with high mortality and no known cure. The dramatic molecular and clinical heterogeneity seen in this tumor has led to attempts to define genetically similar subgroups of GBM with the hope of developing tumor specific therapies targeted to the unique biology within each of these subgroups. Recently, a subset of relatively favorable prognosis GBMs has been identified. These glioma CpG island methylator phenotype, or G-CIMP tumors, have distinct genomic copy number aberrations, DNA methylation patterns, and (mRNA) expression profiles compared to other GBMs. While the standard method for identifying G-CIMP tumors is based on …

Quest For Continuous Improvement: Gathering Feedback And Data Through Multiple Methods To Evaluate And Improve A Library’S Discovery Tool, Jeanne M. Brown

Library Faculty Presentations

Summon at UNLV

• Implemented fall 2011: a web-scale discovery tool
• Expectations for Summon
• Continuous Summon Improvement (CSI)Group

The environment

• User changes
• Library changes
• Vendor changes
• Product changes
• Complex information environment
• Change + complexity = need to assess using multiple streams of feedback

Pls-Rog: Partial Least Squares With Rank Order Of Groups, Hiroyuki Yamamoto

COBRA Preprint Series

Partial least squares (PLS), which is an unsupervised dimensionality reduction method, has been widely used in metabolomics. PLS can separate score depend on groups in a low dimensional subspace. However, this cannot use the information about rank order of groups. This information is often provided in which concentration of administered drugs to animals is gradually varies. In this study, we proposed partial least squares for rank order of groups (PLS-ROG). PLS-ROG can consider both separation and rank order of groups.

Statistical Hypothesis Test Of Factor Loading In Principal Component Analysis And Its Application To Metabolite Set Enrichment Analysis, Hiroyuki Yamamoto, Tamaki Fujimori, Hajime Sato, Gen Ishikawa, Kenjiro Kami, Yoshiaki Ohashi

COBRA Preprint Series

Principal component analysis (PCA) has been widely used to visualize high-dimensional metabolomic data in a two- or three-dimensional subspace. In metabolomics, some metabolites (e.g. top 10 metabolites) have been subjectively selected when using factor loading in PCA, and biological inferences for these metabolites are made. However, this approach is possible to lead biased biological inferences because these metabolites are not objectively selected by statistical criterion. We proposed a statistical procedure to pick up metabolites by statistical hypothesis test of factor loading in PCA and make biological inferences by metabolite set enrichment analysis (MSEA) for these significant metabolites. This procedure depends …

Spectral Cross Correlation As A Supervised Approach For The Analysis Of Complex Raman Datasets: The Case Of Nanoparticles In Biological Cells, Mark Keating, Franck Bonnier, Hugh Byrne

Articles

Spectral Cross-correlation is introduced as a methodology to identify the presence and subcellular distribution of nanoparticles in cells. Raman microscopy is employed to spectroscopically image biological cells previously exposed to polystyrene nanoparticles, as a model for the study of nano-bio interactions. The limitations of previously deployed strategies of K-means clustering analysis and principal component analysis are discussed and a novel methodology of Spectral Cross Correlation Analysis is introduced and compared with the performance of Classical Least Squares Analysis, in both unsupervised and supervised modes. The previous study demonstrated the feasibility of using Raman spectroscopy to map cells and identify polystyrene …