This paper addresses a problem in reporting scientific research. The problem is how to distinguish between justifiable and unjustifiable data selection. Robert Millikan is notorious for an infamous remark that he used all his data when in fact he had used a selection. On this basis he has been accused of fraud. There is a tension here — historians and his defenders see his selection as understandable and legitimate, while current statements about the Responsible Conduct (...) of Research imply his selection was illegitimate. This paper discusses two main issues that arise in assessing his conduct, whether he was intentionally misleading and whether he actually did mislead the scientific community about some facts of nature. It is argued that he was not intentionally misleading, and that it is unlikely that he misled the scientific community. (shrink)

Four experiments investigated the effects of probability manipulations on the indicative four card selection task (Wason, 1966, 1968). All looked at the effects of high and low probability antecedents (p) and consequents (q) on participants' data selections when determining the truth or falsity of a conditional rule, if p then q . Experiments 1 and 2 also manipulated believability. In Experiment 1, 128 participants performed the task using rules with varied contents pretested for probability of occurrence. Probabilistic effects (...) were observed which were partly consistent with some probabilistic accounts but not with non-probabilistic approaches to selection task performance. No effects of believability were observed, a finding replicated in Experiment 2 which used 80 participants with standardised and familiar contents. Some effects in this experiment appeared inconsistent with existing probabilistic approaches. To avoid possible effects of content, Experiments 3 (48 participants) and 4 (20 participants) used abstract material. Both experiments revealed probabilistic effects. In the Discussion we examine the compatibility of these results with the various models of selection task performance. (shrink)

It has been reported as a robust effect that people are likely to select a matching case in the Wason selection task. For example, they usually select the 5 case, in the Wason selection task with the conditional "if an E, then a not-5". This was explained by the matching bias account that people are likely to regard a matching case as relevant to the truth of the conditional (Evans, 1998). However, because a positive concept usually constructs a (...) smaller set than its negative one does (a rarity assumption), it is more effective to get information on the truth of the conditional in a positive set than in a negative set. Thus the optimal data selection account can also explain the effect. The set size of Q and matching by introducing negation were manipulated independently in four experiments. From the results it was inferred that the so-called matching bias was an amalgam of two different cognitive components-relevance judgement by matching and optimal data selection. (shrink)

In this paper, I propose an account that accommodates the possibility of experimentation being exploratory in cases where the procedures necessary to plan and perform an experiment are dependent on the theoretical accounts of the phenomena under investigation. The present account suggests that experimental exploration requires the implementation of an exploratory procedure that serves to extend the range of possible outcomes of an experiment, thereby enabling it to pursue its objectives. Furthermore, I argue that the present account subsumes the notion (...) of exploratory experimentation, which is often attributed in the relevant literature to the works of Friedrich Steinle and Richard Burian, as a particular type of experimental exploration carried out in the special cases where no well-formed theoretical framework of the phenomena under investigation exists. I illustrate the present account in the context of the ATLAS experiment at CERN’s Large Hadron Collider, where the long-sought Higgs boson has been discovered in 2012. I argue that the data selection procedure carried out in the ATLAS experiment illustrates an exploratory procedure in the sense suggested by the present account. I point out that this particular data selection procedure is theory-laden in that its implementation is crucially dependent on the theoretical models of high energy particle physics which the ATLAS experiment is aimed to test. However, I argue that the foregoing procedure is not driven by the above-mentioned theoretical models, but rather by a particular data selection strategy. I conclude that the ATLAS experiment illustrates that, contrary to what previous studies have suggested, there are cases of experimentation in which exploration serves to test theoretical predictions and that theory-ladenness plays an essential role in experimentation being exploratory. (shrink)

Oaksford and Chater (1994) proposed to analyse the Wason selection task as an inductive instead of a deductive task. Applying Bayesian statistics, they concluded that the cards that participants tend to select are those with the highest expected information gain. Therefore, their choices seem rational from the perspective of optimal data selection. We tested a central prediction from the theory in three experiments: card selection frequencies should be sensitive to the subjective probability of occurrence for individual (...) cards. In Experiment 1, expected frequencies of the p- and the q-card were manipulated independently by concepts referring to large vs. small sets. Although the manipulation had an effect on card selection frequencies, there was only a weak correlation between the predicted and the observed patterns. In the second experiment, relative frequencies of individual cards were manipulated more directly by explicit frequency information. In addition, participants estimated probabilities for the four logical cases and of the conditional statement itself. The experimental manipulations strongly affected the probability estimates, but were completely unrelated to card selections. This result was replicated in a third experiment. We conclude that our data provide little support for optimal data selection theory. (shrink)

The research is focused on the role of two related key concepts, namely variables and data, in the impact evaluations of public projects. A difficult task of the evaluators and researchers is to select the appropriate variables to ensure the best model of reality and satisfy the evaluation methods' needs. Therefore, the paper aims to look at the current knowledge and discuss how variables and data could be best used to connect the evaluation models, the particularities of the (...) intervention with the potential of the advanced quantitative assessment methods. The results emphasise that evaluations operate with data with different levels of granularity, as required by the intervention logic. Structuring data in clusters and categories, performing evaluability assessments are useful in assessing data quality and limitations and improving them. In line with the existing literature, we demonstrate that data accessibility is a key constraint and imposes adjustment of the desired evaluation model to a feasible one. While Big Data and Open Data systems significantly improved data quality in evaluations in recent years, blockchain, as a ledger technology with default features related to decentralisation and security, is expected to bring large benefits to evaluation. For evaluators and policymakers, blockchain potential is an area of further research looking for additional advantages that could enhance the use of quantitative methods. (shrink)

Following the rediscovery of Mendelian genetics, food supply pressures and the rapid expansion of crop varieties with defined performance characteristics, international systems were set up throughout the 20 C to regulate the trade of seed, the protection of intellectual property and the sale of productive varieties of key agricultural crops. These systems are a highly connected but largely linear set of processes. System changes are slow to be adopted due to the cascade of effects that structural alteration would have globally. (...) Multi-omic technologies and the subsequent proliferation of data types used within modern breeding, offer the possibility to gain deeper insights into the performance characteristics of varieties. Current integration of data, standards and ownership structures limit their applications for wider purposes, both private and public. We explore how data within and between breeding programmes and the varietal approval and monitoring processes could be made FAIR. We examine what role expanded or aligned programmes of data collection and expanded trait evaluation at the point of varietal registration and evaluation, as well as on farm could have in ensuring the best linkage of public and private data to address some of the challenges society faces over the next 30 years with the required, rapid transition to sustainable agricultural systems. (shrink)

Battin et al examined data on deaths from physician-assisted suicide in Oregon and on PAS and voluntary euthanasia in The Netherlands. This paper reviews the methodology used in their examination and questions the conclusions drawn from it—namely, that there is for the most part ‘no evidence of heightened risk’ to vulnerable people from the legalisation of PAS or VE. This critique focuses on the evidence about PAS in Oregon. It suggests that vulnerability to PAS cannot be categorised simply by (...) reference to race, gender or other socioeconomic status and that the impetus to seek PAS derives from factors, including emotional state, reactions to loss, personality type and situation and possibly to PAS contagion, all factors that apply across the social spectrum. It also argues, on the basis of official reports from the Oregon Health Department on the working of the Oregon Death with Dignity Act since 2008, that, contrary to the conclusions drawn by Battin et al, the highest resort to PAS in Oregon is among the elderly and, on the basis of research published since Battin et al reported, that there is reason to believe that some terminally ill patients in Oregon are taking their own lives with lethal drugs supplied by doctors despite having had depression at the time when they were assessed and cleared for PAS. (shrink)

The prevalence, course and prognosis of diseases in patients referred to tertiary medical centers frequently differ from those treated in primary care settings. Extrapolation of findings from one population to another may therefore be unwarranted. Other factors that contribute to misinterpretation of medical literature include failure to distinguish statistical from clinical significance and advocacy of medical interventions prior to adequate clinical trials.

Preventive vaccination is one of the greatest successes of modern medicine. The SARS-CoV-2 epidemic, during which vaccination is the main method of prevention against death and severe disease, gave rise to a resurgence of anti-vaccine movements. The aim of this study was to analyse the attitudes of students towards vaccination and the COVID-19 pandemic. The statistical analysis was performed with the use of the following data-mining methods: correspondence analysis and basket analysis. The obtained results show that students of medicine (...) are characterized with the highest level of knowledge. Students of other medical faculties, on the other hand, have a significantly less uniform views, as do students of non-medical faculties. (shrink)

Several quantitative techniques for choosing among data models are available. Among these are techniques based on algorithmic information theory, minimum description length theory, and the Akaike information criterion. All these techniques are designed to identify a single model of a data set as being the closest to the truth. I argue, using examples, that many data sets in science show multiple patterns, providing evidence for multiple phenomena. For any such data set, there is more than one (...) data model that must be considered close to the truth. I conclude that, since the established techniques for choosing among data models are unequipped to handle these cases, they cannot be regarded as adequate. ‡I presented a previous version of this paper at the 20th Biennial Meeting of the Philosophy of Science Association, Vancouver, November 2006. I am grateful to the audience for constructive discussion. I thank Leiden University students Marjolein Eysink Smeets and Lenneke Schrier for suggesting the cortisol example, and Remko van der Geest for comments on a draft. †To contact the author, please write to: Faculty of Philosophy, University of Leiden, P.O. Box 9515, 2300 RA Leiden, The Netherlands; e-mail: [email protected]. (shrink)

Trust is a critical component of research: trust in the work of co-workers and colleagues within the scientific community; trust in the work of research scientists by the non-research community. A wide range of factors, including internally and externally generated pressures and practical and personal limitations, affect the research process. The extent to which these factors are understood and appreciated influence the development of trust in scientific research findings.

Schizophrenia has attracted more than its fair share of evolutionary-based theories. The theories involving balancing selection are based on the assumption that the incidence of schizophrenia is invariant across time and place. Modern epidemiology allows us to reject this dogmatic belief. Once variations in the genetic and epidemiological landscape of schizophrenia are acknowledged, more productive research models can be generated. (Published Online November 9 2006).

Recently, big data analytics have gained significant attention in healthcare industry due to generation of massive quantities of data in various forms such as electronic health records, sensors, medical imaging, and pharmaceutical details. However, the data gathered from various sources are intrinsically uncertain owing to noise, incompleteness, and inconsistency. The analysis of such huge data necessitates advanced analytical techniques using machine learning and computational intelligence for effective decision making. To handle data uncertainty in healthcare sector, (...) this article presents a novel metaheuristic rough set-based feature selection with rule-based medical data classification technique on MapReduce framework. The proposed MRSFS-RMDC technique designs a butterfly optimization algorithm for minimal rough set selection. In addition, Hadoop MapReduce is applied to process massive quantity of data. Moreover, a rule-based classification approach named Repeated Incremental Pruning for Error Reduction is used with the inclusion of a set of conditional rules. The RIPPER will scale in a linear way with the number of training records utilized and is suitable to build models with data uncertainty. The proposed MRSFS-RMDC technique is validated using benchmark dataset and the results are inspected under varying aspects. The experimental results highlighted the supremacy of the MRSFS-RMDC technique over the recent state of art methods in terms of different performance measures. The proposed methodology has achieved a higher F-score of 96.49%. (shrink)

When relating genomic data to survival outcomes, there are three main challenges that are the censored survival outcomes, the high-dimensionality of the genomic data, and the non-normality of data. We propose a method to tackle these challenges simultaneously and obtain a robust estimation of detecting significant genes related to survival outcomes based on Accelerated Failure Time model. Specifically, we include a general loss function to the AFT model, adopt model regularization and shrinkage technique, cope with parameters tuning (...) and model selection, and develop an algorithm based on unified Expectation–Maximization approach for easy implementation. Simulation results demonstrate the advantages of the proposed method compared with existing methods when the data has heavy-tailed errors and correlated covariates. Two real case studies on patients are provided to illustrate the application of the proposed method. (shrink)

Reflective equilibrium is often credited with extending the idea of accounting for the data from its familiar home in the sciences to the realm of moral philosophy. But careful consideration of the main concepts of this idea—the data to be accounted for and the kind of accounting it is appropriate to expect of a moral theory—leads to a revised understanding of the “accounting for the data” perspective as it applies to the discipline of moral theory selection. (...) This revised understanding is in tension with reflective equilibrium and actually provides more support for the alternative method of moral theory selection that has been termed ‘practical equilibrium’. (shrink)

This paper proposes a multimodal ethnosemiotic conceptual framework for culturally themed selective Web archiving, taking as a practical example the curation of the London French Special Collection in the UK Web Archive. Its focus on a particular ‘community’ is presented as advantageous in overcoming the sheer scale of data available on the Web; yet, it is argued that these ethnographic boundaries may be flawed if they do not map onto the collective self-perception of the London French. The approach establishes (...) several theoretical meeting points between Pierre Bourdieu’s ethnography and Gunther Kress’s multimodal social semiotics, notably, the foregrounding of practice and the meaning-making potentialities of the everyday; the implications of language and categorisation; the interplay between subject and object; evolving notions of agency, authorship and audience; together with social engagement, and the archive as dynamic process and product. The curation rationale proposed stems from Bourdieu’s three-stage field analysis model, which places a strong emphasis on habitus, considered to be most accurately presented through blogs, yet necessitates its contextualisation within the broader field, through institutional websites, for example, whilst advocating a reflexive awareness of the researcher/curator’s role. This, alongside the Kressian acknowledgement of the inherent multimodality of on-line resources, lends itself convincingly to selection and valuation strategies, whilst the discussion of language, genre, authorship and audience is relevant to the potential cataloguing of Web objects. By conceptualising the culturally themed selective Web-archiving process within the ethnosemiotic framework constructed, concrete recommendations emerge regarding curation, classification and crowd-sourcing. (shrink)

Data fraud and selective reporting both present serious threats to the credibility of science. However, there remains considerable disagreement among scientists about how best to sanction data fraud, and about the ethicality of selective reporting. The public is arguably the largest stakeholder in the reproducibility of science; research is primarily paid for with public funds, and flawed science threatens the public’s welfare. Members of the public are able to make meaningful judgments about the morality of different behaviors using (...) moral intuitions. Legal scholars emphasize that to maintain legitimacy, social control policies must be developed with some consideration given to the public’s moral intuitions. Although there is a large literature on popular attitudes toward science, there is no existing evidence about public opinion on data fraud or selective reporting. We conducted two studies—a survey experiment with a nationwide convenience sample, and a follow-up survey with a representative sample of US adults —to explore community members’ judgments about the morality of data fraud and selective reporting in science. The findings show that community members make a moral distinction between data fraud and selective reporting, but overwhelmingly judge both behaviors to be immoral and deserving of punishment. Community members believe that scientists who commit data fraud or selective reporting should be fired and banned from receiving funding. For data fraud, most Americans support criminal penalties. Results from an ordered logistic regression analysis reveal few demographic and no significant partisan differences in punitiveness toward data fraud. (shrink)

Since the solution to many public health problems depends on research, it is critical for the progress and well-being for the patients that we can trust the scientific literature. Misconduct and poor laboratory practice in science threatens the scientific progress, leads to loss of productivity and increased healthcare costs, and endangers lives of patients. Data duplication may represent one of challenges related to these problems. In order to estimate the frequency of data duplication in life science literature, a (...) systematic screen through 120 original scientific articles published in three different cancer related journals [journal impact factor 20] was completed. The study revealed a surprisingly high proportion of articles containing data duplication. For the IF 20 journals, 25 % of the articles were found to contain data duplications. The IF 5–10 journal showed a comparable proportion. The proportion of articles containing duplicated data was comparable between the three journals and no significant correlation to journal IF was found. The editorial offices representing the journals included in this study and the individual authors of the detected articles were contacted to clarify the individual cases. The editorial offices did not reply and only 1 out of 29 cases were apparently clarified by the authors, although no supporting data was supplied. This study questions the reliability of life science literature, it illustrates that data duplications are widespread and independent of journal impact factor and call for a reform of the current peer review and retraction process of scientific publishing. (shrink)

The evidence compiled in the target article demonstrates that the assumptions of cultural group selection theory are often met, and it is therefore a useful framework for generating plausible hypotheses. However, more can be said about how we can test the predictions of CGS hypotheses against competing explanations using historical, archaeological, and anthropological data.

The development of medicine is based on reliable medical research. This is a process that must be planned in detail and performed in accordance with the accepted study protocol, as any negligence – even a small one – or deviation from the protocol may result in distorted research results and – in consequence – to false conclusions. One of the key stages of research is the selection of the appropriate methodology, particularly in terms of tests that verify the posed (...) hypotheses. Each statistical method has a set of assumptions that have to be met in order for the method to be used. This paper explains why the appropriate selection of methods, verification of the required assumptions, and correct graphical representation of the obtained results are so important from the practical perspective. (shrink)

Two experiments were conducted with variants of Wason's (1966) selection task. The common focus was the effect of differences in the sizes of the sets represented by P and not-Q in assertions of the form _If P then Q_ (conditional) or _All P are Q_ (categorical). Results support the conclusion that such set size differences affect the strategies people adopt when asked to determine, efficiently, the truth or falsity of such assertions, but they do not entirely negate the tendency (...) to select the term represented by P (the antecedent of the conditional or the subject of the categorical) in preference to the term represented by not-Q (the complement of the consequent of the conditional or the complement of the predicate of the categorical). Several possible explanations of this perseverance are considered. Results also support the conclusion that people find it easier to assess categorical assertions than logically equivalent conditional assertions. (shrink)

*PSA 2016, symposium on “Data in Time: Epistemology of Historical Data” organized by Sabina Leonelli, 5 November 2016* *See published version: "Radiocarbon Dating in Archaeology: Triangulation and Traceability" in Data Journeys in the Sciences (2020) - link below* Archaeologists put a premium on pressing “legacy data” into service, given the notoriously selective and destructive nature of their practices of data capture. Legacy data consist of material and records that been assembled over decades, sometimes centuries, (...) often by means and for purposes long since discredited or superseded. The primary strategies by which archaeologists put the data to work for new purposes are, I argue, secondary retrieval, recontextualization, and experimental modelling. I focus here on a particularly telling and complex example of secondary retrieval: the extraction of new data from old by means of radiocarbon dating. This is by no means a straightforward process of retrieving physical samples from legacy data to which 14C techniques can be applied that can, on their own, decisively settle chronological questions. When Libby’s post-war radiocarbon revolution got under way, it was expected to establish an absolute chronology that would render obsolete the local and relative chronologies on which archaeologists had long relied. Transformative though it has been, bringing these tools of physical dating to bear on archaeological problems has been a long, tortuous process, now described as proceeding through two subsequent radiocarbon revolutions. The second was an extended process of calibration by which 14C chronologies were corrected and refined, often against the very lines of evidence they were meant to displace. The most recent, a “pragmatic Bayesian” approach to archaeological dating, is motivated by concern that, no matter how much it is refined, radiocarbon dating cannot on its own resolve the chronological problems that archaeologists address; the challenge, its advocates argue, is to ‘fully integrate archaeological information with 14C dating in order to address archaeologically relevant timescales and episodes’. This is a genre of “robustness” reasoning that illustrates its epistemic risks as well as its appeal. As recent philosophical debate makes clear, it depends on appeals to the convergence of independent lines of evidence that may have more rhetorical than epistemic force and that may be spurious. Drawing on this philosophical literature I identify a set of conditions that must be met if these risks are to be avoided, all of which are an explicit focus of debate in cases of contestation about and reconciliation of chronologies based on legacy data. (shrink)

Abstract:Suppose we want to know whether the ethics of persons with one characteristic differ from the ethics of persons having another characteristic. Self-selection bias occurs if people have control over that characteristic. When there is self-selection bias, we cannot be sure observed differences in ethics are correlated with the characteristic or are the result of individual self-selection. Self-selection bias is germane to many important business ethics questions. In this paper I explain what self-selection bias is, (...) how it relates to business ethics research, and how to correct for it. I also illustrate the correction process in an empirical analysis of the effect of organizational rank on worker ethics. Using data from the European Values Survey, I find that being a supervisor is positively correlated with worker ethics. However, I also find a negative self-selection effect. Workers with relatively lower ethics are selected into supervisory roles. (shrink)

Abstract:For Dewey, growth in the educative process means education that enriches and expands one’s experience as it prepares students for not only a vocation but also entry into and transaction with the world. In few places can we see growth, generally understood, to be occurring as fast as in big data technology. This essay begins with an overview of what big data is, specifically what big data looks like in education as understood through learning management system platforms (...) but also data as understood generally on a college campus through dataveillance, or data surveillance understood as the monitoring or possibility of monitoring of one’s data or online activity. In so doing, this essay acknowledges a selection of privacy concerns and educational challenges resulting from big data. I then proceed to the employment of intelligent technology in education with particular emphasis on algorithms and “algorithmic nudging.” I conclude with a Deweyan analysis of technology as continuous with culture and a discussion about the educational needs for technological environments to aid in the proper cultivation of individuals. (shrink)

Judging similarities among objects, events, and experiences is one of the most basic cognitive abilities, allowing us to make predictions and generalizations. The main assumption in similarity judgment is that people selectively attend to salient features of stimuli and judge their similarities on the basis of the common and distinct features of the stimuli. However, it is unclear how people select features from stimuli and how they weigh features. Here, we present a computational method that helps address these questions. Our (...) procedure combines image-processing techniques with a machine-learning algorithm and assesses feature weights that can account for both similarity and categorization judgment data. Our analysis suggests that a small number of local features are particularly important to explain our behavioral data. (shrink)

Sexual selection processes have received much attention in recent years, attention reflected in interest in human mate preferences. Among these mate preferences are preferences for physical attractiveness. Preferences in and of themselves, however, do not fully explain the nature of the relationships that individuals attain. A tacit negotiation process underlies relationship formation and maintenance. The notion that preferences for physical attractiveness evolved under parasite-driven “good genes” sexual selection leads to predictions about the nature of trade-offs that individuals make (...) between mates’ physical attractiveness and investment potential. These predictions and relevant data are explored, with a primary emphasis on women’s preferences for men’s qualities. In addition, further implications of trade-offs are examined, most notably (a) the impact of environmental variations on the nature of mating and (b) some effects of trade-offs on infidelity and male attempts to control women. (shrink)

Most biologists and some cognitive scientists have independently reached the conclusion that there is no such thing as learning in the traditional “instructive‘ sense. This is, admittedly, a somewhat extreme thesis, but I defend it herein the light of data and theories jointly extracted from biology, especially from evolutionary theory and immunology, and from modern generative grammar. I also point out that the general demise of learning is uncontroversial in the biological sciences, while a similar consensus has not yet (...) been reached in psychology and in linguistics at large. Since many arguments presently offered in defense of learning and in defense of “general intelligence‘ are often based on a distorted picture of human biological evolution, I devote some sections of this paper to a critique of “adaptationism,‘ providing also a sketch of a better evolutionary theory. Moreover, since certain standard arguments presented today as “knock-down‘ in psychology, in linguistics and in artificial intelligence are a perfect replica of those once voiced by biologists in favor of instruction and against selection, I capitalize on these errors of the past to draw some lessons for the present and for the future. (shrink)

The Bell/CHSH inequalities of quantum physics are identical with the inequalities derived in mathematical psychology for the problem of selective influences in cases involving two binary experimental factors and two binary random variables recorded in response to them. The following points are made regarding cognitive science applications: (1) compliance of data with these inequalities is informative only if the data satisfy the requirement known as marginal selectivity; (2) both violations of marginal selectivity and violations of the Bell/CHSH inequalities (...) are interpretable as indicating that at least one of the two responses is influenced by both experimental factors. (shrink)

In this paper, we analyse the relation between the use of environmental data in contemporary health sciences and related conceptualisations and operationalisations of the notion of environment. We consider three case studies that exemplify a different selection of environmental data and mode of data integration in data-intensive epidemiology. We argue that the diversification of data sources, their increase in scale and scope, and the application of novel analytic tools have brought about three significant conceptual (...) shifts. First, we discuss the EXPOsOMICS project, an attempt to integrate genomic and environmental data which suggests a reframing of the boundaries between external and internal environments. Second, we explore the MEDMI platform, whose efforts to combine health, environmental and climate data instantiate a reframing and expansion of environmental exposure. Third, we illustrate how extracting epidemiological insights from extensive social data collected by the CIDACS institute yields innovative attributions of causal power to environmental factors. Identifying these shifts highlights the benefits and opportunities of new environmental data, as well as the challenges that such tools bring to understanding and fostering health. It also emphasises the constraints that data selection and accessibility pose to scientific imagination, including how researchers frame key concepts in health-related research. (shrink)

There is much effort nowadays to protect communication networks against different cybersecurity attacks (which are more and more sophisticated) that look for systems’ vulnerabilities they could exploit for malicious purposes. Network Intrusion Detection Systems (NIDSs) are popular tools to detect and classify such attacks, most of them based on ML models. However, ML-based NIDSs cannot be trained by feeding them with network traffic data as it is. Thus, a Feature Engineering (FE) process plays a crucial role transforming network traffic (...) raw data onto derived one suitable for ML models. In this work, we study the effects of applying one such FE technique in different ways on the performance of two ML models (linear and non-linear) and their selected features. This the Feature as a Counter approach. The derived observations are computed from either with the same number of raw samples, (batch-based approaches) or by aggregating them by time intervals (timestamp-based approach). Results show that there is no significant differences between the proposed approaches neither in the performance of the models nor in the selected features that validate our proposal making it feasible to be widely used as a standard FE method. (shrink)

Trust is a critical component of research: trust in the work of co-workers and colleagues within the scientific community; trust in the work of research scientists by the non-research community. A wide range of factors, including internally and externally generated pressures and practical and personal limitations, affect the research process. The extent to which these factors are understood and appreciated influence the development of trust in scientific research findings.

In order to reduce the dimensionality of parameter space and enhance out-of-sample forecasting performance, this research compares regularization techniques with Autometrics in time-series modeling. We mainly focus on comparing weighted lag adaptive LASSO with Autometrics, but as a benchmark, we estimate other popular regularization methods LASSO, AdaLASSO, SCAD, and MCP. For analytical comparison, we implement Monte Carlo simulation and assess the performance of these techniques in terms of out-of-sample Root Mean Square Error, Gauge, and Potency. The comparison is assessed with (...) varying autocorrelation coefficients and sample sizes. The simulation experiment indicates that, compared to Autometrics and other regularization approaches, the WLAdaLASSO outperforms the others in covariate selection and forecasting, especially when there is a greater linear dependency between predictors. In contrast, the computational efficiency of Autometrics decreases with a strong linear dependency between predictors. However, under the large sample and weak linear dependency between predictors, the Autometrics potency ⟶ 1 and gauge ⟶ α. In contrast, LASSO, AdaLASSO, SCAD, and MCP select more covariates and possess higher RMSE than Autometrics and WLAdaLASSO. To compare the considered techniques, we made the Generalized Unidentified Model for covariate selection and out-of-sample forecasting for the trade balance of Pakistan. We train the model on 1985–2015 observations and 2016–2020 observations as test data for the out-of-sample forecast. (shrink)