An Exploration of Regression-Based Data Mining Techniques Using Super Computation

Regression analysis is intended to be used when the researcher seeks to test a given hypothesis against a data set. Unfortunately, in many applications it is either not possible to specify a hypothesis, typically because the research is in a very early stage, or it is not desirable to form a hypothesis, typically because the number of potential explanatory variables is very large. In these cases, researchers have resorted either to overt data mining techniques such as stepwise regression, or covert data mining techniques such as running variations on regression models prior to running the final model (also known as “data peeking”). While data mining side-steps the need to form a hypothesis, it is highly susceptible to generating spurious results. This paper draws on the known properties of OLS estimators in the presence of omitted and extraneous variable models to propose a procedure for data mining that attempts to distinguish between parameter estimates that are significant due to an underlying structural relationship and those that are significant due to random chance.exhaustive, regression, all subsets, stepwise, data mining

Analyzing Three-Dimensional Panel Data of Forecasts

With the proliferation of quality multi-dimensional surveys, it becomes increasingly important for researchers to employ an econometric framework in which these data can be properly analyzed and put to their maximum use. In this chapter we have summarized such a framework developed in Davies and Lahiri (1995, 1999), and illustrated some of the uses of these multi-dimensional panel data. In particular, we have characterized the adaptive expectations mechanism in the context of broader rational and implicit expectations hypotheses, and suggested ways of testing one hypothesis over the others. We find that, under the adaptive expectations model, a forecaster who fully adapts to new information is equivalent to a forecaster whose forecast bias increases linearly with the forecast horizon. A multi-dimensional forecast panel also provides the means to distinguish between anticipated and unanticipated changes in the forecast target as well as volatilities associated with the anticipated and unanticipated changes. We show that a proper identification of anticipated changes and their perceived volatilities are critical to the correct understanding and estimation of forecast uncertainty. In the absence of such rich forecast data, researchers have typically used the variance of forecast errors as proxies for shocks. It is the perceived volatility of the anticipated change and not the (subsequently-observed) volatility of the target variable or the unanticipated change that should condition forecast uncertainty. This is because forecast uncertainty is formed when a forecast is made, and hence anything that was unknown to the forecaster when the forecast was made should not be a factor in determining forecast uncertainty. This finding has important implications on how to estimate forecast uncertainty in real time and how to construct a measure of average historical uncertainty, cf. Lahiri and Sheng (2010a). Finally, we show how the Rational Expectations hypothesis should be tested by constructing an appropriate variance-covariance matrix of the forecast errors when a specific type of multidimensional panel data is available.

Transparency, Performance, and Agency Budgets: A Rational Expectations Modeling Approach

Existing research suggests that bureaucrats’ optimal behavior is to maximize their agency’s budgets, but does not account for information imperfections nor explore the tactics bureaucrats employ in maximizing their budgets. Drawing on the rational expectations literature, we propose a new theoretical model that describes the behaviors of politicians who, using imperfect information, judge an agency’s performance, and bureaucrats who, by varying the agency’s transparency, alter the degree of information imperfection and so influence the politicians’ abilities to judge the agency’s performance. We then fit data from the government’s Performance Accountability Reports and the Scorecard data set to our model and obtain empirical results that are consistent with what our theoretical model predicts.bureaucracy; agency; budget; budget maximization; transparency; performance; imperfect information; Government Performance Reports Act; Scorecard

Dentifrices : an update

Objectives; The objective of this paper was to review the published evidence concerning the efficacy and potential for adverse reactions of modern dentifrices toothpastes. Data sources; Publications cited on MEDLINE since 1990. Some further pre-1990 publications are also referenced. Data selection; Studies concerning the efficacy of dentifrices and their components and any related putative adverse incidents. Data extraction; Papers were scrutinised for scientific and trial data. Data synthesis; Data concerning the efficacy of dentifrice components were summarised. Conclusions; The efficacy of fluoride salts in dentifrices in reducing dental caries is well established. Toothpastes, containing triclosan, are effective in improving plaque control, gingivitis and periodontal health. Other toothpaste formulations are effective in reducing the formation of calculus, extrinsic tooth stain, dentine sensitivity and oral malodour. The consumer now has available a range of toothpastes which deliver oral health benefits. Adverse reactions to toothpastes are rare but should be considered in unexplained skin or respiratory allergies and gingival or lip lesions

Chemometrics for ion mobility spectrometry data:Recent advances and future prospects

Contains fulltext : 161386.pdf (publisher's version ) (Open Access)Historically, advances in the field of ion mobility spectrometry have been hindered by the variation in measured signals between instruments developed by different research laboratories or manufacturers. This has triggered the development and application of chemometric techniques able to reveal and analyze precious information content of ion mobility spectra. Recent advances in multidimensional coupling of ion mobility spectrometry to chromatography and mass spectrometry has created new, unique challenges for data processing, yielding high-dimensional, megavariate datasets. In this paper, a complete overview of available chemometric techniques used in the analysis of ion mobility spectrometry data is given. We describe the current state-of-the-art of ion mobility spectrometry data analysis comprising datasets with different complexities and two different scopes of data analysis, i.e. targeted and non-targeted analyte analyses. Two main steps of data analysis are considered: data preprocessing and pattern recognition. A detailed description of recent advances in chemometric techniques is provided for these steps, together with a list of interesting applications. We demonstrate that chemometric techniques have a significant contribution to the recent and great expansion of ion mobility spectrometry technology into different application fields. We conclude that well-thought out, comprehensive data analysis strategies are currently emerging, including several chemometric techniques and addressing different data challenges. In our opinion, this trend will continue in the near future, stimulating developments in ion mobility spectrometry instrumentation even further

A multivariate approach to investigate the NMR CPMG pulse sequence for analysing low MW species in polymers

Detection and quantification of low molecular weight components in polymeric samples via nuclear magnetic resonance (NMR) spectroscopy can be difficult due to overlapping signal caused by line broadening characteristics of polymers. A way of overcoming this problem could be the exploitation of the difference in relaxation between small molecules and macromolecular species, such as the application of a T2 filter by using the Carr–Purcell–Meiboom–Gill (CPMG) spin-echo pulse sequence. This technique, largely exploited in metabolomics studies, is applied here to material sciences. A Design of Experiments approach was used for evaluating the effect of different acquisition parameters (relaxation delay, echo time and number of cycles) and sample-related ones (concentration and polymer molecular weight) on selected responses, with a particular interest in performing a reliable quantitative analysis. Polymeric samples containing small molecules were analysed by NMR with and without the application of the filter, and analysis of variance was used to identify the most influential parameters. Results indicated that increasing the polymer concentration, hence sample viscosity, further attenuates polymer signals in CPMG experiments because the T2 of those signals tends to decrease with increasing viscosity. The signal-to-noise ratio measured for small molecules can undergo a minimum loss when specific parameters are chosen in relation to the polymer molecular weight. Furthermore, the difference in dynamics between aliphatic and aromatic nuclei, as well as between mobile and stiff polymers, translates into different results in terms of polymer signal reduction, suggesting that the relaxation filter can also be used for obtaining information on the polymer structure

Optimising access to best practice primary health care: a systematic review

Ensuring that Australians have access to health care is an integral component of Australian health care policy. Growing awareness of the importance of primary health care (PHC) in delivering equitable and cost-effective care is creating interest in better understanding and addressing access to best practice PHC. This review examines evidence from the published literature on potential interventions to enhance access to ‘best practice’.The research reported in this paper is a project of the Australian Primary Health Care Research Institute which is supported by a grant from the Australian Government Department of Health and Ageing under the Primary Health Care Research Evaluation and Development Strategy

5G in the Wild:Performance of C-Band 5G-NR in Rural Low-Power Fixed Wireless Access

In this paper, we have evaluated the performance of one of the first community-led rural applications of standalone 5G for fixed wireless access on the n77 band in the UK. This was achieved through a full-stack holistic monitoring platform, evaluating the overall performance and quality of experience of connections in the network. Our results show that 5G n77 networks can provide sub-gigabit connectivity in line of sight applications in rural areas, even when accounting for the impact stemming from infrastructural constraints. We have evidenced that our platform has identified opportunities to improve performance and capacity

Optimizing access to best practice primary health care: a systematic review

The research reported in this paper is a project of the Australian Primary Health Care Research Institute, which is supported by a grant from the Australian Government Department of Health and Ageing under the Primary Health Care Research, Evaluation and Development Strategy