Information Content Of Data Sets Research Articles

The Elements of Measurement.

A fundamental structure for the measurement process is developed. Measurement is defined as the determination of the number of units of a particular quantity or property presented by the measured object or sample. Measurement is distinguished from qualitative observation and data interpretation. The limited sources of measurement numbers (scalar, digital, and counting readouts) reveal that all measurements except manual counting require a measurement device. Measurement systems consist of devices which convert one form of data encoding to another. They begin with the sensor and proceed through intermediate conversion devices until the readout device produces numbers. There are three classes of conversion devices: input (sensor), intermediate, and readout. The characteristics of the conversion devices in a system determine the overall system characteristics. Counting measurements have several unique characteristics. In automated counting, conversion devices are used in event detection, discrimination, and boundary setting. Variance in counting arises from event detection, boundary setting, and random event occurrence. Entire measurement systems or conversion device subsystems may operate by null comparison. Conversion devices in null comparison systems have a different arrangement. The time relationship of multiple or successive measurements affects the information content of data sets. Time-correlation of acquired data is critical in all stimulus-response measurement systems such as chromatography and time-of-flight mass spectrometry and in many subsystems such as lock-in amplifiers and box-car integrators.

Information content in data sets: A review of methods for interrogation and model comparison

Abstract In this review we discuss methodology to ascertain the amount of information in given data sets with respect to determination of model parameters with desired levels of uncertainty. We do this in the context of least squares (ordinary, weighted, iterative reweighted weighted or “generalized”, etc.) based inverse problem formulations. The ideas are illustrated with several examples of interest in the biological and environmental sciences.

Asteroseismology of red giants: From analysing light curves to estimating ages

AbstractAsteroseismology has started to provide constraints on stellar properties that will be essential to accurately reconstruct the history of the Milky Way. Here we look at the information content in data sets representing current and future space missions (CoRoT, Kepler, K2, TESS, and PLATO) for red giant stars. We describe techniques for extracting the information in the frequency power spectrum and apply these techniques to Kepler data sets of different observing length to represent the different space missions. We demonstrate that for KIC 12008916, a low‐luminosity red giant branch star, we can extract useful information from all data sets, and for all but the shortest data set we obtain good constraint on the g‐mode period spacing and core rotation rates. We discuss how the high precision in these parameters will constrain the stellar properties of stellar radius, distance, mass and age. We show that high precision can be achieved in mass and hence age when values of the g‐mode period spacing are available. We caution that tests to establish the accuracy of asteroseismic masses and ages are still “work in progress”.

Information content in data sets for a nucleated-polymerization model

We illustrate the use of statistical tools (asymptotic theories of standard error quantification using appropriate statistical models, bootstrapping, and model comparison techniques) in addition to sensitivity analysis that may be employed to determine the information content in data sets. We do this in the context of recent models [S. Prigent, A. Ballesta, F. Charles, N. Lenuzza, P. Gabriel, L.M. Tine, H. Rezaei, and M. Doumic, An efficient kinetic model for assemblies of amyloid fibrils and its application to polyglutamine aggregation, PLoS ONE 7 (2012), e43273. doi:10.1371/journal.pone.0043273.] for nucleated polymerization in proteins, about which very little is known regarding the underlying mechanisms; thus, the methodology we develop here may be of great help to experimentalists. We conclude that the investigated data sets will support with reasonable levels of uncertainty only the estimation of the parameters related to the early steps of the aggregation process.

Model Comparison Tests to Determine Data Information Content.

In the context of inverse or parameter estimation problems we demonstrate the use of statistically based model comparison tests in several examples of practical interest. In these examples we are interested in questions related to information content of a particular given data set and whether the data will support a more complicated model to describe it. In the first example we compare fits for several different models to describe simple decay in a size histogram for aggregates in amyloid fibril formation. In a second example we investigate whether the information content in data sets for the pest Lygus hesperus in cotton fields as it is currently collected is sufficient to support a model in which one distinguishes between nymphs and adults. Finally in a third example with data for patients having undergone an organ transplant, we question whether the data content is sufficient to estimate more than 5 of the fundamental parameters in a particular dynamic model.

Probe combination in large galaxy surveys: application of Fisher information and Shannon entropy to weak lensing

This paper is aimed at developing a better understanding of the structure of the information that in contained in galaxy surveys, so as to find optimal ways to combine observables from such surveys. We first show how Jaynes' Maximal Entropy Principle allows us, in the general case, to express the Fisher information content of data sets in terms of the curvature of the Shannon entropy surface with respect to the relevant observables. This allows us to understand the Fisher information content of a data set, once a physical model is specified, independently of the specific way that the data will be processed, and without any assumptions of Gaussianity. This includes as a special case the standard Fisher matrix prescriptions for Gaussian variables widely used in the cosmological community, for instance for power spectra extraction. As an application of this approach, we evaluate the prospects of a joint analysis of weak lensing tracers up to second order in the shapes distortions, in the case that the noise in each probe can be effectively treated as model independent. These include the magnification, the two ellipticity and the four flexion fields. At the two point level, we show that the only effect of treating these observables in combination is a simple scale dependent decrease of the noise contaminating the accessible spectrum of the lensing E-mode. We provide simple bounds to its extraction by a combination of such probes, as well as its quantitative evaluation when the correlations between the noise variables for any two such probes can be neglected.

High Throughput Screening Informatics

High throughput screening (HTS), an industrial effort to leverage developments in the areas of modern robotics, data analysis and control software, liquid handling devices, and sensitive detectors, has played a pivotal role in the drug discovery process, allowing researchers to efficiently screen millions of compounds to identify tractable small molecule modulators of a given biological process or disease state and advance them into high quality leads. As HTS throughput has significantly increased the volume, complexity, and information content of datasets, lead discovery research demands a clear corporate strategy for scientific computing and subsequent establishment of robust enterprise-wide (usually global) informatics platforms, which enable complicated HTS work flows, facilitate HTS data mining, and drive effective decision-making. The purpose of this review is, from the data analysis and handling perspective, to examine key elements in HTS operations and some essential data-related activities supporting or interfacing the screening process, and outline properties that various enabling software should have. Additionally, some general advice for corporate managers with system procurement responsibilities is offered.

The Effect of Microaggregation Procedures on the Estimation of Linear Models: A Simulation Study

Summary Microaggregation is a set of procedures that distort empirical data in order to guarantee their factual anonymity. At the same time the information content of data sets should not be reduced too much and should still be useful for scientific research. This paper investigates the effect of microaggregation on the estimation of a linear regression by ordinary least squares. It studies, by way of an extensive simulation experiment, the bias of the slope parameter estimator induced by various microaggregation techniques. Some microaggregation procedures lead to consistent estimates while others imply an asymptotic bias for the estimator.

A Priori Estimation of Phylogenetic Information Conserved in Aligned Sequences

A new phenomenological approach to explorative data analysis, the estimation of spectra of supporting positions, allows the search for conserved tracks left by phylogeny in DNA sequences. Spectra of supporting positions can be generated without reference to a tree topology or a model of sequence evolution and are therefore an ideal tool for a priori estimation of information content of data sets. Analysis of published 18S rDNA alignments shows that signal to noise relationship varies greatly in a way not detected by conventional tree-construction methods.