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Articles 1 - 30 of 10938

Full-Text Articles in Physical Sciences and Mathematics

Neural Shrubs: Using Neural Networks To Improve Decision Trees, Kyle Caudle, Randy Hoover, Aaron Alphonsus Feb 2019

Neural Shrubs: Using Neural Networks To Improve Decision Trees, Kyle Caudle, Randy Hoover, Aaron Alphonsus

SDSU Data Science Symposium

Decision trees are a method commonly used in machine learning to either predict a categorical response or a continuous response variable. Once the tree partitions the space, the response is either determined by the majority vote – classification trees, or by averaging the response values – regression trees. This research builds a standard regression tree and then instead of averaging the responses, we train a neural network to determine the response value. We have found that our approach typically increases the predicative capability of the decision tree. We have 2 demonstrations of this approach that we wish to present as a poster ...


Multi-Linear Algebraic Eigendecompositions And Their Application In Data Science, Randy Hoover, Kyle Caudle Dr., Karen Braman Dr. Feb 2019

Multi-Linear Algebraic Eigendecompositions And Their Application In Data Science, Randy Hoover, Kyle Caudle Dr., Karen Braman Dr.

SDSU Data Science Symposium

Multi-dimensional data analysis has seen increased interest in recent years. With more and more data arriving as 2-dimensional arrays (images) as opposed to 1-dimensioanl arrays (signals), new methods for dimensionality reduction, data analysis, and machine learning have been pursued. Most notably have been the Canonical Decompositions/Parallel Factors (commonly referred to as CP) and Tucker decompositions (commonly regarded as a high order SVD: HOSVD). In the current research we present an alternate method for computing singular value and eigenvalue decompositions on multi-way data through an algebra of circulants and illustrate their application to two well-known machine learning methods: Multi-Linear Principal ...


Predicting Unplanned Medical Visits Among Patients With Diabetes Using Machine Learning, Arielle Selya, Eric L. Johnson Feb 2019

Predicting Unplanned Medical Visits Among Patients With Diabetes Using Machine Learning, Arielle Selya, Eric L. Johnson

SDSU Data Science Symposium

Diabetes poses a variety of medical complications to patients, resulting in a high rate of unplanned medical visits, which are costly to patients and healthcare providers alike. However, unplanned medical visits by their nature are very difficult to predict. The current project draws upon electronic health records (EMR’s) of adult patients with diabetes who received care at Sanford Health between 2014 and 2017. Various machine learning methods were used to predict which patients have had an unplanned medical visit based on a variety of EMR variables (age, BMI, blood pressure, # of prescriptions, # of diagnoses on problem list, A1C, HDL ...


Volleyball Overhead Swing Volume And Injury Frequency Over The Course Of A Season, Heather Wolfe, Katherine Poole, Alejandro G. Villasante Tezanos, Robert A. English, Timothy L. Uhl Feb 2019

Volleyball Overhead Swing Volume And Injury Frequency Over The Course Of A Season, Heather Wolfe, Katherine Poole, Alejandro G. Villasante Tezanos, Robert A. English, Timothy L. Uhl

Statistics Faculty Publications

Background: Overuse injuries are common in volleyball; however, few studies exist that quantify the workload of a volleyball athlete in a season. The relationship between workload and shoulder injury has not been extensively studied in women's collegiate volleyball athletes.

Hypothesis/Purpose: This study aims to quantify shoulder workloads by counting overhead swings during practice and matches. The purpose of the current study is to provide a complete depiction of typical overhead swings, serves, and hits, which occur in both practices and matches. The primary hypothesis was that significantly more swings will occur in practices compared to matches. The secondary ...


Level Crossing Simulation Of A Queueing Model, Zhanxuan Ding Jan 2019

Level Crossing Simulation Of A Queueing Model, Zhanxuan Ding

Major Papers

Simulation of the level crossing method will be used to find approximations of the distribution of the workload for several queueing models. In particular, three different type of queueing models, with different methods of handling workload bound thresholds, will be considered. Simulation applied to workload bound thresholds is new work.


Infinite Sums, Products, And Urn Models, Yiyan Ni Jan 2019

Infinite Sums, Products, And Urn Models, Yiyan Ni

Major Papers

This paper considers an urn and its evolution in discrete time steps. The

urn initially has two different colored balls(blue and red). We discuss different

cases where k blue balls (k = 1, 2, 3, ... ) will be added (or removed) at every

step if a blue ball is withdrawn, based on the goal of eventually withdrawing a

red ball P(R eventually). We compute the probability of eventually withdrawing

a red ball with two different methods–one using infinite sums and other using

infinite products. One advantage of this is that we can obtain P(R eventually) in

a complex ...


An Evaluation Of Training Size Impact On Validation Accuracy For Optimized Convolutional Neural Networks, Jostein Barry-Straume, Adam Tschannen, Daniel W. Engels, Edward Fine Jan 2019

An Evaluation Of Training Size Impact On Validation Accuracy For Optimized Convolutional Neural Networks, Jostein Barry-Straume, Adam Tschannen, Daniel W. Engels, Edward Fine

SMU Data Science Review

In this paper, we present an evaluation of training size impact on validation accuracy for an optimized Convolutional Neural Network (CNN). CNNs are currently the state-of-the-art architecture for object classification tasks. We used Amazon’s machine learning ecosystem to train and test 648 models to find the optimal hyperparameters with which to apply a CNN towards the Fashion-MNIST (Mixed National Institute of Standards and Technology) dataset. We were able to realize a validation accuracy of 90% by using only 40% of the original data. We found that hidden layers appear to have had zero impact on validation accuracy, whereas the ...


Comparisons Of Performance Between Quantum And Classical Machine Learning, Christopher Havenstein, Damarcus Thomas, Swami Chandrasekaran Jan 2019

Comparisons Of Performance Between Quantum And Classical Machine Learning, Christopher Havenstein, Damarcus Thomas, Swami Chandrasekaran

SMU Data Science Review

In this paper, we present a performance comparison of machine learning algorithms executed on traditional and quantum computers. Quantum computing has potential of achieving incredible results for certain types of problems, and we explore if it can be applied to machine learning. First, we identified quantum machine learning algorithms with reproducible code and had classical machine learning counterparts. Then, we found relevant data sets with which we tested the comparable quantum and classical machine learning algorithm's performance. We evaluated performance with algorithm execution time and accuracy. We found that quantum variational support vector machines in some cases had higher ...


Political Profiling Using Feature Engineering And Nlp, Chiranjeevi Mallavarapu, Ramya Mandava, Sabitri Kc, Ginger M. Holt Jan 2019

Political Profiling Using Feature Engineering And Nlp, Chiranjeevi Mallavarapu, Ramya Mandava, Sabitri Kc, Ginger M. Holt

SMU Data Science Review

Public surveys are predominantly used when forecasting election outcomes. While the approach has had significant successes, the surveys have had their failures as well, especially when it comes to accuracy and reliability. As a result, it becomes challenging for political parties to spend their campaign budgets in a manner that facilitates the growth of a favorable and verifiable public opinion. Consequently, it is critical that a more accurate methodology to predict election outcome is developed. In this paper, we present an evaluation of the impact of utilizing dynamic public data on predicting the outcome of elections. Our model yielded a ...


Pedestrian Safety -- Fundamental To A Walkable City, Joshua Herrera, Patrick Mcdevitt, Preeti Swaminathan, Raghuram Srinivas Jan 2019

Pedestrian Safety -- Fundamental To A Walkable City, Joshua Herrera, Patrick Mcdevitt, Preeti Swaminathan, Raghuram Srinivas

SMU Data Science Review

In this paper, we present a method to identify urban areas with a higher likelihood of pedestrian safety related events. Pedestrian safety related events are pedestrian-vehicle interactions that result in fatalities, injuries, accidents without injury, or near--misses between pedestrians and vehicles. To develop a solution to this problem of identifying likely event locations, we assemble data, primarily from the City of Cincinnati and Hamilton County, that include safety reports from a five year period, geographic information for these events, citizen survey of pedestrian reported concerns, non-emergency requests for service for any cause in the city, property values and public transportation ...


Improving Vix Futures Forecasts Using Machine Learning Methods, James Hosker, Slobodan Djurdjevic, Hieu Nguyen, Robert Slater Jan 2019

Improving Vix Futures Forecasts Using Machine Learning Methods, James Hosker, Slobodan Djurdjevic, Hieu Nguyen, Robert Slater

SMU Data Science Review

The problem of forecasting market volatility is a difficult task for most fund managers. Volatility forecasts are used for risk management, alpha (risk) trading, and the reduction of trading friction. Improving the forecasts of future market volatility assists fund managers in adding or reducing risk in their portfolios as well as in increasing hedges to protect their portfolios in anticipation of a market sell-off event. Our analysis compares three existing financial models that forecast future market volatility using the Chicago Board Options Exchange Volatility Index (VIX) to six machine/deep learning supervised regression methods. This analysis determines which models provide ...


Improving Gas Well Economics With Intelligent Plunger Lift Optimization Techniques, Atsu Atakpa, Emmanuel Farrugia, Ryan Tyree, Daniel W. Engels, Charles Sparks Jan 2019

Improving Gas Well Economics With Intelligent Plunger Lift Optimization Techniques, Atsu Atakpa, Emmanuel Farrugia, Ryan Tyree, Daniel W. Engels, Charles Sparks

SMU Data Science Review

In this paper, we present an approach to reducing bottom hole plunger dwell time for artificial lift systems. Lift systems are used in a process to remove contaminants from a natural gas well. A plunger is a mechanical device used to deliquefy natural gas wells by removing contaminants in the form of water, oil, wax, and sand from the wellbore. These contaminants decrease bottom-hole pressure which in turn hampers gas production by forming a physical barrier within the well tubing. As the plunger descends through the well it emits sounds which are recorded at the surface by an echo-meter that ...


Gene Co-Expression Networks Analysis Reveal Novel Molecular Endotypes In Alpha-1 Antitrypsin Deficiency, Jen-Hwa Chu, Wenlan Zang Jan 2019

Gene Co-Expression Networks Analysis Reveal Novel Molecular Endotypes In Alpha-1 Antitrypsin Deficiency, Jen-Hwa Chu, Wenlan Zang

Yale Day of Data

Rationale:Alpha-1 antitrypsin deficiency (AATD) is a genetic condition that predisposes to early onset pulmonary emphysema and airways obstruction. The exact mechanism through which AATD leads to lung disease is incompletely understood.

Objectives: To investigate the effect of AAT genotype and augmentation therapy on bronchoalveolar lavage (BAL) and peripheral blood mononuclear cells (PBMC) transcriptome, while examining the link between gene expression profiles, and clinical features of AATD.

Methods: We performed RNA-Seq on RNA extracted from BAL and PBMC on samples obtained from 89 AATD patients enrolled in the Genomic Research in Alpha-1 Antitrypsin Deficiency and Sarcoidosis (GRADS) study. Differential gene ...


Non-Invasive Analysis Of The Sputum Transcriptome Discriminates Clinical Phenotypes Of Asthma, Xiting Yan Jan 2019

Non-Invasive Analysis Of The Sputum Transcriptome Discriminates Clinical Phenotypes Of Asthma, Xiting Yan

Yale Day of Data

Whole transcriptome wide gene expression profiles in the sputum and circulation from 100 asthma patients were measured using the Affymetrix HuGene 1.0ST arrays. Unsupervised clustering analysis based on pathways from KEGG were used to identify TEA clusters of patients from the sputum gene expression profiles. The identified TEA clusters have significantly different pre-bronchodilator FEV1, bronchodilator responsiveness, exhaled nitric oxide levels, history of hospitalization for asthma and history of intubation. Evaluation of TEA clusters in children from Asthma BRIDGE cohort confirmed the identified differences in intubation and hospitalization. Furthermore, evaluation of the TH2 gene signatures suggested a much lower prevalence ...


A Novel Pathway-Based Distance Score Enhances Assessment Of Disease Heterogeneity In Gene Expression, Yunqing Liu, Xiting Yan Jan 2019

A Novel Pathway-Based Distance Score Enhances Assessment Of Disease Heterogeneity In Gene Expression, Yunqing Liu, Xiting Yan

Yale Day of Data

Distance-based unsupervised clustering of gene expression data is commonly used to identify heterogeneity in biologic samples. However, high noise levels in gene expression data and the relatively high correlation between genes are often encountered, so traditional distances such as Euclidean distance may not be effective at discriminating the biological differences between samples. In this study, we developed a novel computational method to assess the biological differences based on pathways by assuming that ontologically defined biological pathways in biologically similar samples have similar behavior. Application of this distance score results in more accurate, robust, and biologically meaningful clustering results in both ...


A Latent Spatial Piecewise Exponential Model For Interval-Censored Disease Surveillance Data With Time-Varying Covariates And Misclassification, Yaxuan Sun, Chong Wang, William Q. Meeker, Max Morris, Marisa L. Rotolo, Jeffery Zimmerman Jan 2019

A Latent Spatial Piecewise Exponential Model For Interval-Censored Disease Surveillance Data With Time-Varying Covariates And Misclassification, Yaxuan Sun, Chong Wang, William Q. Meeker, Max Morris, Marisa L. Rotolo, Jeffery Zimmerman

Veterinary Diagnostic and Production Animal Medicine Publications

Understanding the dynamics of disease spread is critical to achieving effective animal disease surveillance. A major challenge in modeling disease spread is the fact that the true disease status cannot be known with certainty due to the imperfect diagnostic sensitivity and specificity of the tests used to generate the disease surveillance data. Other challenges in modeling such data include interval censoring, relating disease spread to distance between units, and incorporating time-varying covariates, which are the unobserved disease statuses. We propose a latent spatial piecewise exponential model (PEX) with misclassification of events to address the challenges in modeling such disease surveillance ...


Collaborative Efforts To Forecast Seasonal Influenza In The United States, 2015–2016, Craig J. Mcgowan, Jarad Niemi, Nehemias Ulloa, Katie Will, Et Al. Jan 2019

Collaborative Efforts To Forecast Seasonal Influenza In The United States, 2015–2016, Craig J. Mcgowan, Jarad Niemi, Nehemias Ulloa, Katie Will, Et Al.

Statistics Publications

Since 2013, the Centers for Disease Control and Prevention (CDC) has hosted an annual influenza season forecasting challenge. The 2015–2016 challenge consisted of weekly probabilistic forecasts of multiple targets, including fourteen models submitted by eleven teams. Forecast skill was evaluated using a modified logarithmic score. We averaged submitted forecasts into a mean ensemble model and compared them against predictions based on historical trends. Forecast skill was highest for seasonal peak intensity and short-term forecasts, while forecast skill for timing of season onset and peak week was generally low. Higher forecast skill was associated with team participation in previous influenza ...


Toward Using High-Frequency Coastal Radars For Calibration Of S-Ais Based Ocean Vessel Tracking Models, Ben Freidrich Jan 2019

Toward Using High-Frequency Coastal Radars For Calibration Of S-Ais Based Ocean Vessel Tracking Models, Ben Freidrich

Theses and Dissertations (Comprehensive)

Most of the world relies on ships for transportation, shipping, and tourism. Automatic Identification System messages are transmitted from ships and provide a wealth of positional data on these open ocean vessels. This data is being utilized to determine the optimal path for ships, as well as predicting where a ship may be going in the near future. It has only been in the past decade that Automatic Identification Systems (AIS) signals have been easily received with satellites (S-AIS) so there have been few studies that look at using available information and pairing it with the new abundance of ship ...


General Population Normative Data For The Eortc Qlq-C30 Health-Related Quality Of Life Questionnaire Based On 15,386 Persons Across 13 European Countries, Canada And The Unites States, S. Nolte, G. Liegl, M. A. Petersen, N. K. Aaronson, A. Costantini, P. M. Fayers, M. Groenvold, B. Holzner, C. D. Johnson, G Kemmler, K. A. Tomaszewski, A. Waldmann, T. E. Young, Matthias S. F. Rose Jan 2019

General Population Normative Data For The Eortc Qlq-C30 Health-Related Quality Of Life Questionnaire Based On 15,386 Persons Across 13 European Countries, Canada And The Unites States, S. Nolte, G. Liegl, M. A. Petersen, N. K. Aaronson, A. Costantini, P. M. Fayers, M. Groenvold, B. Holzner, C. D. Johnson, G Kemmler, K. A. Tomaszewski, A. Waldmann, T. E. Young, Matthias S. F. Rose

Open Access Articles

OBJECTIVE: The European Organisation for Research and Treatment of Cancer (EORTC) QLQ-C30 health-related quality of life questionnaire is one of the most widely used cancer-specific health-related quality of life questionnaires worldwide. General population norm data can facilitate the interpretation of QLQ-C30 data obtained from cancer patients. This study aimed at systematically collecting norm data from the general population to develop European QLQ-C30 norm scores and to generate comparable norm data for individual countries in Europe and North America.

METHODS: We collected QLQ-C30 data from the general population across 11 European Union (EU) countries, Russia, Turkey, Canada and United States (n ...


Establishing The European Norm For The Health-Related Quality Of Life Domains Of The Computer-Adaptive Test Eortc Cat Core, G. Liegl, M. A. Petersen, M. Groenvold, N. K. Aaronson, A. Costantini, P. M. Fayers, B. Holzner, C. D. Johnson, G. Kemmler, K. A. Tomaszewski, A. Waldmann, T. E. Young, Matthias S. F. Rose, S. Nolte Jan 2019

Establishing The European Norm For The Health-Related Quality Of Life Domains Of The Computer-Adaptive Test Eortc Cat Core, G. Liegl, M. A. Petersen, M. Groenvold, N. K. Aaronson, A. Costantini, P. M. Fayers, B. Holzner, C. D. Johnson, G. Kemmler, K. A. Tomaszewski, A. Waldmann, T. E. Young, Matthias S. F. Rose, S. Nolte

Open Access Articles

OBJECTIVE: The computer-adaptive test (CAT) of the European Organisation for Research and Treatment of Cancer (EORTC), the EORTC CAT Core, assesses the same 15 domains as the EORTC QLQ-C30 health-related quality of life questionnaire but with increased precision, efficiency, measurement range and flexibility. CAT parameters for estimating scores have been established based on clinical data from cancer patients. This study aimed at establishing the European Norm for each CAT domain based on general population data.

METHODS: We collected representative general population data across 11 European Union (EU) countries, Russia, Turkey, Canada and the United States (n > /= 1000/country; stratified by ...


Numeracy And Social Justice: A Wide, Deep, And Longstanding Intersection, Kira Hamman, Victor Piercey, Samuel L. Tunstall Jan 2019

Numeracy And Social Justice: A Wide, Deep, And Longstanding Intersection, Kira Hamman, Victor Piercey, Samuel L. Tunstall

Numeracy

We discuss the connection between the numeracy and social justice movements both in historical context and in its modern incarnation. The intersection between numeracy and social justice encompasses a wide variety of disciplines and quantitative topics, but within that variety there are important commonalities. We examine the importance of sound quantitative measures for understanding social issues and the necessity of interdisciplinary collaboration in this work. Particular reference is made to the papers in the first part of the Numeracy special collection on social justice, which appear in this issue.


Umsl Faculty Expertise Dec 2018

Umsl Faculty Expertise

Anne Folta Fish

Research Specializations: Advanced Theory Construction; Cognitive-behavioral; Editing; Evaluation of Health Care Systems Internationally; Evaluation of Methods; Evaluation (Content Analysis, Experimental Studies, Quasi-experimental Design, and Randomized Control Studies); Exercise Hypertension; Health Problems; Nursing; Nursing Evaluation; Prevention Research; Randomized Clinical Trials; and Treatment Research


Model Misspecification And Assumption Violations With The Linear Mixed Model: A Meta-Analysis, Brandon Lebeau, Yoon Ah Song, Wei Cheng Liu Dec 2018

Model Misspecification And Assumption Violations With The Linear Mixed Model: A Meta-Analysis, Brandon Lebeau, Yoon Ah Song, Wei Cheng Liu

Department of Psychological and Quantitative Foundations Publications

This meta analysis attempts to synthesize the Monte Carlo literature for the linear mixed model under a longitudinal framework. The empirical type I error rate will serve as the effect size and Monte Carlo simulation conditions will be coded to serve as moderator variables. The type I error rate for the fixed and random effects will be explored as the primary dependent variable. Effect sizes were coded from 13 studies, resulting in a total of 4,002 and 621 effect sizes for fixed and random effects. Meta-regression and proportional odds models were used to explore variation in the empirical type ...


Power In Pairs: Assessing The Statistical Value Of Paired Samples In Tests For Differential Expression, John R. Stevens, Jennifer S. Herrick, Roger K. Wolff, Martha L. Slattery Dec 2018

Power In Pairs: Assessing The Statistical Value Of Paired Samples In Tests For Differential Expression, John R. Stevens, Jennifer S. Herrick, Roger K. Wolff, Martha L. Slattery

Mathematics and Statistics Faculty Publications

Background: When genomics researchers design a high-throughput study to test for differential expression, some biological systems and research questions provide opportunities to use paired samples from subjects, and researchers can plan for a certain proportion of subjects to have paired samples. We consider the effect of this paired samples proportion on the statistical power of the study, using characteristics of both count (RNA-Seq) and continuous (microarray) expression data from a colorectal cancer study.

Results: We demonstrate that a higher proportion of subjects with paired samples yields higher statistical power, for various total numbers of samples, and for various strengths of ...


Application Of Bradford’S Law Of Scattering On Research Publication In Astronomy & Astrophysics Of India, Satish Kumar, Senthilkumar R. Dec 2018

Application Of Bradford’S Law Of Scattering On Research Publication In Astronomy & Astrophysics Of India, Satish Kumar, Senthilkumar R.

Library Philosophy and Practice (e-journal)

The present study is focused on examining the application of Bradford’s law of scattering on research articles published in the field of Astronomy & Astrophysics by Indian scientist during 1988-2017. The bibliographic data was retrieved from Web of Science (WoS) bibliographic data base for different period of time. Total 18,877 journal’s article have been published by Indian scientist in the field of Astronomy & Astrophysics during 1988-2017 which was further retrieved and analyzed separately for different blocks of 10 years as well as for 30 years consolidated too. The core journal of the field was identified. The Bradford law ...


A Proficient Two-Stage Stratified Randomized Response Strategy, Tanveer A. Tarray, Housila P. Singh Dec 2018

A Proficient Two-Stage Stratified Randomized Response Strategy, Tanveer A. Tarray, Housila P. Singh

Journal of Modern Applied Statistical Methods

A stratified randomized response model based on R. Singh, Singh, Mangat, and Tracy (1995) improved two-stage randomized response strategy is proposed. It has an optimal allocation and large gain in precision. Conditions are obtained under which the proposed model is more efficient than R. Singh et al. (1995) and H. P. Singh and Tarray (2015) models. Numerical illustrations are also given in support of the present study.


Re-Describing Surface Roughness, Vincent Wagner Dec 2018

Re-Describing Surface Roughness, Vincent Wagner

Essential Studies UNDergraduate Showcase

The purpose of this project is to explore a non-traditional method of identifying and describing variance in data. The original goal was to provide a more useful description of surface roughness for use in calculating pressure loss due to pipe friction in the oil and gas industry. This approach uses simple trigonometric calculations to capture more information about the point to point variance of a given data set, as well as information related to the ratio of measured length vs total contact length. This method utilizes steps similar to the bootstrap method in statistics, however, rather than sampling a data ...


Instances Of Influenza In The United States Visualized, Parth Patel Dec 2018

Instances Of Influenza In The United States Visualized, Parth Patel

Publications and Research

The Tycho Project collects large data sets related to healthcare and in particular, instances and geographical information of diseases. We look at the instance counts and locations of Influenza from 1919-1951 across the United States. We hope to find seasonal and geographical insight to the spread of the disease.


Extended Method For Several Dichotomous Covariates To Estimate The Instantaneous Risk Function Of The Aalen Additive Model, Luciane Teixeira Passos Giarola, Mario Javier Ferrua Vivanco, Marcelo Angelo Cirillo, Fortunato Silva Menezes Dec 2018

Extended Method For Several Dichotomous Covariates To Estimate The Instantaneous Risk Function Of The Aalen Additive Model, Luciane Teixeira Passos Giarola, Mario Javier Ferrua Vivanco, Marcelo Angelo Cirillo, Fortunato Silva Menezes

Journal of Modern Applied Statistical Methods

The instantaneous risk function of Aalen’s model is estimated considering dichotomous covariates, using parametric accumulated risk functions to smooth cumulative risk of Aalen by grouping the individuals into sets named parcels. This methodology can be used for data with dichotomous covariates.


Simple Unbalanced Ranked Set Sampling For Mean Estimation Of Response Variable Of Developmental Programs, Girish Chandra, Dinesh S. Bhoj, Rajiv Pandey Dec 2018

Simple Unbalanced Ranked Set Sampling For Mean Estimation Of Response Variable Of Developmental Programs, Girish Chandra, Dinesh S. Bhoj, Rajiv Pandey

Journal of Modern Applied Statistical Methods

An unbalanced ranked set sampling (RSS) procedure on the skewed survey variable is proposed to estimate the population mean of a response variable from the area of developmental programs which are generally implemented under different phases. It is based on the unbalanced RSS under linear impacts of the program and is compared with the estimators based on simple random sampling (SRS) and balanced RSS. It is shown that the relative precision of the proposed estimator is higher than those of the estimators based on SRS and balanced RSS for three chosen skewed distributions of survey variables.