Boundary Relations between Amount of Information and Decision Fidelity for Data Coding and Analysis Models

McDonald, Erickson, and McDonald (2000) and Murtaugh (2000) have presented two interesting views of analysis and interpretation of data from before-after control-impact (BACI) studies. In my opinion, the general message they convey to the reader is that analysis of data from a BACI design, in combination with professional expertise, can add useful evidence as long as one is prudent about the resulting interpretations. Whether scientists who analyze BACI data will indeed use caution in interpretation, particularly in making statements regarding cause and effect, remains to be seen. McDonald et al.'s (2000) study is application specific, taking the reader through an analysis of bird count data from the Exxon Valdez oil spill (EVOS). The authors prudently phrase their interpretation in terms of a weight of evidence argument rather than the much more difficult one of causation. I see the primary usefulness of this paper as that of encouraging scientists to use more complex models to realistically describe count data through time. (Using these models in data analysis has been considerably enhanced by the increasing availability of various types of computer software.) Particularly with biological data, a conventional first step is to try an additive model on untransformed data or an additive model on log-transformed data with the hope that the log transform restores the assumptions of variance homogeneity and normally distributed error terms. The presence of data recorded on the same units through time necessitates that the Huynh-Feldt condition (also known as sphericity) holds in order to use a conventional repeated measures approach. In case this sphericity condition does not hold, there are references to make conservative adjustments to the usual F-tests. (I do wonder just how many people would take the trouble to test these assumptions and do the proper corrections if such corrections were not already built into the

In this thesis, we study two random models with various applications in data analysis. For our first model, we investigate subspaces spanned by biased random vectors. The underlying random model is motivated by applications in computational biology, where one aims at computing a low-rank matrix factorization involving a binary factor. In a random model with adjustable expected sparsity of the binary factor, we show for a large class of random binary factors that the corresponding factorization problem is uniquely solvable with high probability. In data analysis, such uniqueness results are of particular interest; ambiguous solutions often lack interpretability and do not give an insight into the structure of the underlying data. For proving uniqueness in this random model, small ball probability estimates are a key ingredient. Since to the best of our knowledge, there are no such estimate suitable for our application, we prove an extension of the famous Lemma of Littlewood and Offord. Hereby, we also discover a connection between the matrix factorization problem at hand and the notion of Sperner families. In the second part of this thesis, we will investigate a model for randomized ultrasonic data in nondestructive testing. Here, we aim at accelerating the data acquisition process by superposing ultrasonic measurements with random time shifts. To this end, we will first study the effects of randomized ultrasonic measurements in the context of the Synthetic Aperture Focusing Technique (SAFT), a widely used defect imaging method. By adapting SAFT to our random data model, we will significantly improve its performance for randomized data. In this way, for sparse defects and with high probability, we achieve better defect reconstructions as with SAFT applied to deterministic ultrasonic data acquired in the same amount of time.

In the recent decades, remote sensing data are rapidly growing in size and variety, and considered as “big geo data” because of their huge data volume, significant heterogeneity and challenge of fast analysis. In the traditional remote sensing analysis workflows, the data transfer for downloading raw image files to local workstations often costs a lot of time and slows down the data analysis workflows. Because results of remote sensing data analysis models are usually much smaller than raw data to be processed, “on-demand processing”, which tries to upload data analysis models and execute them “near” where data stores, can significantly accelerate the execution of remote sensing analysis workflows. In this paper, a framework for on-demand remote sensing data analysis is proposed based on three-layered architecture; XML/JSON based runtime environment description; and on-demand model deployment methods. The evaluation on a prototype system shows that on-demand processing framework accelerates the execution of analysis models in 2.8 ~ 12.7 times by reducing data transfers, especially for those analysis workflows which transfer data through low bandwidth Internet. By on-demand processing, classical remote sensing data service systems can evolve into remote sensing data processing infrastructures, which provide IaaS (Infrastructure-as-a-Service) and PaaS (Platform-as-a-Service) services, and make it possible to exchange knowledge among scientists by sharing models. Furthermore, a remote sensing data analysis platform for carbon satellites is designed based on the on-demand processing proposed by this paper and will soon be implemented under the support of SunWay-TaihuLight, the world’s most powerful super computer.

In many data evaluation procedures for particle measuring devices and in filtration models, spherical particles are assumed. However, significant fractions of aerosol particles are agglomerates of small primary spheres. The morphology of particles in filtration processes may not be known a priori and if the filtration data are processed with wrong assumption, errors can be induced. In this work, we have quantified such errors for the case of open-structured agglomerates. Filtration efficiency tests with polydisperse silver nanoparticle agglomerates and their sintered spheres were performed. After the sintering process, particles with a compact structure with the shape close to a sphere are obtained, which are referred to as sintered spheres in the present study. The testing method involved generation of particulate forms, passing the particles through the testing section, and measurement of the particle number concentrations and size distributions before and after the filter. Measurements of the aerosols upstream and downstream of the filter were conducted using scanning mobility particle sizers (SMPS, TSI Inc.), which covered the rage from 10 to 480 nm. Particles were additionally characterized from the electron microscopic images and the average primary particle size was determined to be 16.8 nm. The number-size distribution curves were obtained and used for penetration calculation. The penetration was dependent on the particle size and morphology. Silver-sintered spheres were captured with a lower efficiency than agglomerates with the same mobility diameter because of the stronger interception effect for agglomerates. Data analysis of the number-size distribution for agglomerates was processed based on sphere assumption and using the model for open-structured agglomerates developed by Lall and Friedlander. The efficiencies based on total concentrations of number, surface and volume were affected when the agglomerate model was used. The effect was weakest for the total number efficiency and strongest for the total volume efficiency. Filtration efficiency curves for agglomerates were additionally plotted as a function of the volume equivalent diameter or number of primary particles per agglomerate, because the latter parameters provide more information for the agglomerate structure.

The research was conducted to know the impact of changes in distribution strategies and prices on buying decisions made by retail stores or minimarkets due to government policies on social restrictions of the COVID-19. This study uses quantitative research with population data distribution in one of Indonesia's major cities. It uses a structural model in data analysis with all latent variables linked. They were measuring instruments used using Smart PLS. This study found that with changes in distribution strategies by combining all types of intensive, exclusive, and selective, significant results were obtained at prices and purchasing decisions by customers. The main finding in the study was that the implementation of distribution strategies for government policy adjustments was very effective in reaching customers in all corners of the city so that price adjustments and customer purchase decisions could occur on an ongoing basis.

[1] Water is essential to life; therefore, when humanity asks howmuch fresh water there is, the answer better be right. Answering this question correctly is one of the primary goals of hydrologic science. We do this through development of hydrologic theory, advancing hydrologic analysis, and improving hydrologic measurements. Theory constructs the framework of our investigations and analysis extracts information from our measurements, but after the dust settles, our understanding must be based on observations. The value of our measurements depends on the intrinsic capabilities and limitations of our sensors and on making the best decisions of what tomeasure (where, andwhen, and how) to challenge our theories and improve our understanding. As a result, the adequacy of observations must be assessed in the context of the question to be addressed. [2] The need for informative hydrologic measurements has never been greater. Humanity is in the midst of a radical global hydrologic experiment. It is critical that we quantify and understand the trajectory of change in the distribution of water in response to a changing climate. Of direct and practical interest to all people is: will the patterns of water availability in the future be significantly different than those that we have come to depend upon? Meeting this need requires accurate and complete measurements of the current conditions to provide a baseline against which changes will be assessed. In addition, we will have to continually improve our measurements, analyses, and theories to quantify, understand, and adapt to our changing world. [3] We believe that this special section ofWater Resources Research on Hydrologic Measurement Methods marks a turning point in appreciation of the essential and unfinished business of learning how to get the right data upon which our understanding of hydrology rests. At first look, some of the questions that our science is asking may seem straightforward, but as is shown in this collection of almost 50 papers, getting the answers requires insight, ingenuity, and persistence. Readers of this special section will find approaches to determining answers to the following questions. How much of a rainfall evaporates [Heitman et al., 2008]? How much hits the ground [Friesen et al., 2008] and infiltrates [Kamai et al., 2008]? What is the flow in a river [Le Coz et al., 2008; Muste et al., 2008; Nihei and Kimizu, 2008]? How much sediment is the river water carrying [Gray and Gartner, 2009]? How much snow is on the ground and what is its condition [Fassnacht et al., 2009; Lundquist and Lott, 2008]? How much water is stored in the subsurface [Gehman et al., 2009]? These findings are joined by discussions of new technologies (e.g., lasers, ultrasonics, microradio networks, and fiber optics) to ‘‘see’’ water and to sense its movement [Robinson et al., 2009; Scheuermann et al., 2009; Trubilowicz et al., 2009; Tyler et al., 2009]. Other authors [e.g.,Abdu et al., 2008; Bradford et al., 2009; Crook et al., 2008] discuss the necessary bridge between measurement and analysis (e.g., tomographic analysis, exhaustive data checking, and assimilation of models in data analysis) to ensure that as much information as possible is extracted from the data. [4] What becomes obvious when reading this collection of papers is that while technology has transformed our ability to obtain and interpret quantitative observations, the full potential of our technologies are only realized when hydrologists combine dedicated attention, dogged resolve, and deep appreciation of the desired capabilities and inherent limitations of sensors to their investigations. Equally obvious is the joy that drives most scientists, including hydrologists: the rare gift of being present at the ah ha moment, when new measurements challenge old beliefs and spark discovery. [5] With this special section, Water Resources Research concretely reaffirms its recognition of the primacy of hydrologic measurement. Into the future,Water Resources Research will continue to provide authors with a venue for their discoveries of how to better quantify our precious common resource. As editors of this collection, we find great satisfaction in seeing the volume and quality of work dedicated to the advancement of hydrologic understanding through measurement. We thank all the contributors, reviewers, and the journal’s remarkable staff for their tireless efforts that made this collection possible.

Manifold models in data analysis and signal processing have become more prominent in recent years. In this paper, we will look at one of the main tasks of modern signal processing, namely, at analog-to-digital (A/D) conversion in connection with a simple manifold model (circle). We will focus on Sigma-Delta modulation which is a popular method for A/D conversion of bandlimited signals that employs coarse quantization coupled with oversampling. Classical Sigma-Delta schemes would provide mismatches and large errors at the initialization point if the signal to be converted is defined on a circle. In this paper, our goal is to get around these problems for higher order Sigma-Delta schemes. Our results show how to design an update for the second and third order schemes based on the reconstruction error analysis such that for the updated scheme the reconstruction error is improved.

The purpose of this paper is to share some practical applications of advanced probabilistic models in reliability data analysis. In particular, we will focus on reliability models with fixed and time-dependent covariates. While these models are popular in biological and medical studies, their application in engineering reliability data analysis is still limited. As a special advanced topic, we also discuss a new approach to model/estimate MTTF using Pade approximation.

We believe the great interpretability of fuzzy models allow fuzzy-based methods to play a very important role in Microarray gene expression data analysis, but the advantages offered by fuzzy-based techniques in this application have not yet been fully explored in the literature. In this paper, we construct Multi-Objective Evolutionary Algorithms based Interpretable Fuzzy (MOEAIF) models for microarray gene expression data analysis. Our novel fuzzy models can significantly decrease the model complexity, and automatically balance the accuracy and interpretability of the models. The experimental studies have shown that relatively simple and small fuzzy rule bases, with satisfactory classification performance, have been successful found for challenging microarray gene expression datasets.

The Universitat Politècnica de València and its Faculty of Business Administration and Management have created a new intensification, named, "Intelligent Data Analysis", that provides the student with sufficient knowledge to integrate data analysis in the sometimes routine tasks of a company.The statistical, computer and ICT-related skills obtained through the Business Administration and Management degree are enhanced with more advanced statiscal models for multivariate data analysis and with R language programming, which is very suitable for such data analysis. All these skills are acquired under the Project-Based Learning methodology.This project's main achievement has been the coordination between the different subjects of the intensification to use the same software, which has resulted in a continuity for the way in which students work with RStudio, R, and Rmakdown. This has provided them a high level of management and integration of data analysis in the students’ work routines which will later aid them to become more qualified professionals.

On the statistical analysis of ambient ozone data when measurements are missing

Although there have been several analyses for identifying cancer-associated pathways, based on gene expression data, most of these are based on single pathway analyses, and thus do not consider correlations between pathways. In this paper, we propose a hierarchical structural component model for pathway analysis of gene expression data (HisCoM-PAGE), which accounts for the hierarchical structure of genes and pathways, as well as the correlations among pathways. Specifically, HisCoM-PAGE focuses on the survival phenotype and identifies its associated pathways. Moreover, its application to real biological data analysis of pancreatic cancer data demonstrated that HisCoM-PAGE could successfully identify pathways associated with pancreatic cancer prognosis. Simulation studies comparing the performance of HisCoM-PAGE with other competing methods such as Gene Set Enrichment Analysis (GSEA), Global Test, and Wald-type Test showed HisCoM-PAGE to have the highest power to detect causal pathways in most simulation scenarios.

Exploration on Data Collection and Analysis System Based on Integrated SIFT Algorithm

The purpose of this chapter is to provide you with the ability to perform the second phase of the Training Evaluation Process -- Develop Evaluation Procedures. These activities encompass taking the Evaluation Plan and developing data collection instruments and analysis models necessary for conducting a successful training evaluation. Development of evaluation instruments and their corresponding analysis models are essential to a successful evaluation. These evaluation tools, if constructed correctly, collect and summarize course results so that you can answer the questions posed in the Evaluation Plan. The instruments must be easy to use and convey a sense of professionalism by the evaluator.

To identify pathways associated with survival phenotypes using gene expression data, we recently proposed the hierarchical structural component model for pathway analysis of gene expression data (HisCoM-PAGE) method. The HisCoM-PAGE software can consider hierarchical structural relationships between genes and pathways and analyze multiple pathways simultaneously. It can be applied to various types of gene expression data, such as microarray data or RNA sequencing data. We expect that the HisCoM-PAGE software will make our method more easily accessible to researchers who want to perform pathway analysis for survival times.