Alternative Statistical Approach Research Articles

Constrained Hybrid Monte Carlo Sampling Made Simple for Chemical Reaction Simulations.

Most electrochemical reactions should be studied under a grand canonical ensemble condition with a constant potential and/or a constant pH value. Free energy profiles provide key insights into understanding the reaction mechanisms. However, many molecular dynamics (MD)-based theoretical studies for electrochemical reactions did not employ an exact grand canonical ensemble sampling scheme for the free energy calculations, partially due to the issues of discontinuous trajectories induced by the particle-number variations during MD simulations. An alternative statistical sampling approach, the Monte Carlo (MC) method, is naturally appropriate for the open-system simulations if we focus on the thermodynamic properties. An advanced MC scheme, the hybrid Monte Carlo (HMC) method, which can efficiently sample the configurations of a system with large degrees of freedom, however, has limitations in the constrained-sampling applications. In this work, we propose an adjusted constrained HMC method to compute free energy profiles using the thermodynamic integration (TI) method. The key idea of the method for handling the constraint in TI is to integrate the reaction coordinate and sample the rest degrees of freedom by two types of MC schemes, the HMC scheme and the Metropolis algorithm with unbiased trials (M(RT)2-UB). We test the proposed method on three different systems involving two kinds of reaction coordinates, which are the distance between two particles and the difference of particles' distances, and compare the results to those generated by the constrained M(RT)2-UB method serving as benchmarks. We show that our proposed method has the advantages of high sampling efficiency and convenience of implementation, and the accuracy is justified as well. In addition, we show in the third test system that the proposed constrained HMC method can be combined with the path integral method to consider the nuclear quantum effects, indicating a broader application scenario of the sampling method reported in this work.

The Characterization of Dry Fermented Sausages under the "Chorizo Zamorano" Quality Label: The Application of an Alternative Statistical Approach.

The characterization of quality brand meat products, such as "Chorizo Zamorano" dry fermented sausages, involves a wide range of data which makes it necessary to use alternative statistical methodologies. In this study, the feasibility of the Categorical Principal Components Analysis as a multivariate non-linear technique for the characterization of "Chorizo Zamorano" was assessed. The data analyzed were those of eight commercial brands covered by the quality mark over an eight-year period (2013-2020) and included parameters of the physicochemical composition and organoleptic properties of the product. The results showed that "Chorizo Zamorano" has an average moisture content (28.28%), high protein (38.38%) and fat (51.05%) contents, and a very low carbohydrate concentration (1.52%). Results showed that the fat and protein content and the sensory parameters related to external and internal odor appeared to be the studied variables with the greatest influence on the classification of the products according to their quality.

A framework for a structured problem-solving approach with experimental design as the focus for industrial processing environment

This article focuses on developing a framework for industrial environments to provide a structured problem-solving approach based on experimentation as a basis to assist analysts and management for strategic decision making for process improvement. The process for developing this generic framework was through an analytical process improvement process case study for a company in an industrial environment. The research methodology includes an interpretivist approach followed by a positivist approach and ends with a constructivism worldview due to the Experimental design approach design depicted by this framework. The summarized goals were, expanding Design of Experiments (DOE) as a statistical approach to complement existing methods and methodologies used for Data Mining, validate the integrity of data through a refining process and applying DOE in combination with traditional Data Mining techniques. The importance for developing this framework was to experiment with historical data, based on real process data then to predict future process behaviour, using full and fractional DOE design scenarios which allows the analyst not to have a one-dimensional analytical approach but to evaluate which design fits the data best. No risk of costly process failures due to experimenting into the unknown by utilizing historic data by applying experimentation when evaluating different processing scenarios for possible product improvement and to provide an alternative statistical approach for Data Mining in an industrial environment for screening independent and dependent variables for a DOE model.

Malaria attributable fractions with changing transmission intensity: Bayesian latent class vs logistic models

BackgroundAsymptomatic carriage of malaria parasites is common in high transmission intensity areas and confounds clinical case definitions for research studies. This is important for investigations that aim to identify immune correlates of protection from clinical malaria. The proportion of fevers attributable to malaria parasites is widely used to define different thresholds of parasite density associated with febrile episodes. The varying intensity of malaria transmission was investigated to check whether it had a significant impact on the parasite density thresholds. The same dataset was used to explore an alternative statistical approach, using the probability of developing fevers as a choice over threshold cut-offs. The former has been reported to increase predictive power.MethodsData from children monitored longitudinally between 2005 and 2017 from Junju and Chonyi in Kilifi, Kenya were used. Performance comparison of Bayesian-latent class and logistic power models in estimating malaria attributable fractions and probabilities of having fever given a parasite density with changing malaria transmission intensity was done using Junju cohort. Zero-inflated beta regressions were used to assess the impact of using probabilities to evaluate anti-merozoite antibodies as correlates of protection, compared with multilevel binary regression using data from Chonyi and Junju.ResultsMalaria transmission intensity declined from over 49% to 5% between 2006 and 2017, respectively. During this period, malaria attributable fraction varied between 27–59% using logistic regression compared to 10–36% with the Bayesian latent class approach. Both models estimated similar patterns of fevers attributable to malaria with changing transmission intensities. The Bayesian latent class model performed well in estimating the probabilities of having fever, while the latter was efficient in determining the parasite density threshold. However, compared to the logistic power model, the Bayesian algorithm yielded lower estimates for both attributable fractions and probabilities of fever. In modelling the association of merozoite antibodies and clinical malaria, both approaches resulted in comparable estimates, but the utilization of probabilities had a better statistical fit.ConclusionsMalaria attributable fractions, varied with an overall decline in the malaria transmission intensity in this setting but did not significantly impact the outcomes of analyses aimed at identifying immune correlates of protection. These data confirm the statistical advantage of using probabilities over binary data.

Polychoric Correlation With Ordinal Data in Nursing Research.

Measures in nursing research frequently use Likert scales that yield ordinal data. Confirmatory factor analysis using Pearson correlations commonly applies to such data, although this violates ordinal scale assumptions. The aim of this study was to illustrate the application of polychoric correlations and polychoric confirmatory factor analysis as a valid alternative statistical approach using data on family members' perceived support from nurses as an exemplar. A primary analysis of cross-sectional data from a sample of 800 participants using data collected with the Iceland-Family Perceived Support Questionnaire was conducted using polychoric versus Pearson correlations, analysis of variance, and confirmatory factor analysis. A two-factor measurement model was compatible with data from family members in the Ugandan care settings. Two contextual factors (cognitive and emotional support) constituted the family support measurement model. A factor correlation indicated that the two factors reflected distinct but closely related aspects of family support. Polychoric correlation revealed 13.8% (range: 5.5%-25.2%) higher correlations compared to Pearson correlations. Moreover, the polychoric agreed with the data, whereas the Pearson confirmatory factor analysis did not fit based on multiple statistical criteria. Analyses indicated a difference in emotional and cognitive support perception across two family characteristics: education and relationship to the patient. A polychoric correlation suggests stronger associations, and consequently, the approach can be more credible with an ordinal Likert scale than Pearson correlations. Hence, polychoric confirmatory factor analysis can address a larger proportion of variance. In nursing research, polychoric confirmatory factor analysis can confidently be utilized when conducting confirmatory factor analysis of ordinal variables in Likert scales. Furthermore, when a Pearson confirmatory factor analysis is used for ordinal Likert scales, the researcher should carefully evaluate the difference between the two approaches and justify their methodological choice. Even though we do not suggest dispensing with Pearson correlations entirely, we recommend using polychoric correlation for ordinal Likert scales.

Modeling credit risk with a multi‐stage hybrid model: An alternative statistical approach

AbstractThis paper examines the impact of hybridizations on the classification performances of sophisticated machine learning classifiers such as gradient boosting (GB, TreeNet®) and random forest (RF) using multi‐stage hybrid models. The empirical findings confirm that, overall, hybrid model GB (X*Di; ŶDi, LR), which consists of TreeNet® combined with logistic regression along with a new dependent variable, offers significantly superior accuracy compared to the baselines and other hybrid classifiers. However, the performances of hybrid classifiers are not consistent across all types of datasets. For low‐dimensional data, the constructed models consistently outperform the base classifiers; however, on high dimensional data, the classification outcomes provide little evidence of improvement and in certain cases, they underperform the baseline models. These findings have relevance for the analysis of high‐ and low‐dimensional credit risk, small and medium enterprises, agricultural credits, and so on. Furthermore, the example credit risk scenario and its outcomes provide an alternative path for hybrid and machine learning approaches to be applied to more general applications in accounting and finance fields.

Population-based estimates of age and comorbidity specific life expectancy: a first application in Swedish males

IntroductionFor clinical decision-making, an estimate of remaining lifetime is needed to assess benefit against harm of a treatment during the remaining lifespan. Here, we describe how to predict life expectancy based on age, Charlson Comorbidity Index (CCI) and a Drug Comorbidity Index (DCI), whilst also considering potential future changes in CCI and DCI using population-based data on Swedish men.MethodsSimulations based on annual updates of vital status, CCI and DCI were used to estimate life expectancy at population level. The probabilities of these transitions were determined from generalised linear models using prostate cancer-free comparison men in PCBaSe Sweden. A simulation was performed for each combination of age, CCI, and DCI. Survival curves were created and compared to observed survival. Life expectancy was then calculated as the area under the simulated survival curve.ResultsThere was good agreement between observed and simulated survival curves for most ages and comorbidities, except for younger men. With increasing age and comorbidity, there was a decrease in life expectancy. Cross-validation based on six regions in Sweden also showed that simulated and observed survival was similar.ConclusionOur proposed method provides an alternative statistical approach to estimate life expectancy at population level based on age and comorbidity assessed by routinely collected information on diagnoses and filled prescriptions available in nationwide health care registers.

Collisional energy transfer in the CO-CO system.

An accurate determination of the physical conditions in astrophysical environments relies on the modeling of molecular spectra. In such environments, densities can be so low (n ≪ 1010 cm−3) that local thermodynamical equilibrium conditions cannot be maintained. Hence, radiative and collisional properties of molecules are needed to correctly model molecular spectra. For comets at large heliocentric distances, the production of carbon monoxide (CO) gas is found to be larger than the production of water, so that molecular excitation will be induced by collisions with CO molecules. This paper presents new scattering calculations for the collisional energy transfer in CO–CO collisions. Using the quantum coupled states approach, cross sections and rate coefficients are provided between the first 37 rotational states of the CO–CO system. Cross sections were calculated for energies up to 800 cm−1, and excitation rate coefficients were derived for temperatures up to 100 K. In comparison with data available in the literature, significant differences were found, especially for the dominant transitions. Due to the high cost of the calculations, we also investigated the possibility of using an alternative statistical approach to extend our calculations both in terms of rotational states and temperatures considered. The use of these new collisional data should help in accurately deriving the physical conditions in CO-dominated comets.

Může Bayesův faktor nahradit P hodnotu?

The P value was introduced as a value to evaluate the results of statistical tests. The basic concept originated in the 1920s, and after the Second World War it was significantly expanded. For about the last three decades there has been intense discussion about the problematic features of the P value concept and its use in science, and voices calling to abolish use of the P value are growing louder. In addition, suggestions have been made for alternative procedures that could replace or supplement the P value. Statisticians have tried to invent an indicator similar to the P value, but without its weaknesses. There are many of these options. Besides alternatives within the classical statistical testing paradigm, the use of an alternative statistical approach, so-called Bayesian statistics, is increasingly being discussed. An example of a moderate recommendation is that of using the Bayes factor, essentially an analogue of the P value in the Bayesian world. The aim of this article is to present the Bayes factor in detail, to describe its similarities and dissimilarities with the P value, and discuss the possibilities of its calculation. In addition to computational procedures, a detailed discussion of the weaknesses of the Bayes factor is also included.

Normative reference equations of airway dynamics assessed by whole-body plethysmography during spontaneous breathing evaluated in infants, children, and adults.

Effective specific airway resistance (sReff), its reciprocal the effective specific airway conductance (sGeff) are computed as ratios between the integral of the resistive aerodynamic work of breathing (sWOB) and the integral of the tidal flow/volume loop, the reciprocal, respectively. Unfortunately, reference equations to obtain normative values for sReff, sGeff, and sWOB are not yet available. To assess reference equations for sWOB, sReff, and sGeff during tidal breathing at resting level in healthy infants, children, and adults by a multidimensional model. Retrospectively exported data were collected from databases of five Swiss lung function centers, in which plethysmography (Jaeger Würzburg, Germany) was performed for the assessment of airway dynamics, static lung volumes, and forced breathing flow‐volume loops, in a collective of 28 healthy infants, 47 children, and 273 adults. From this cohort, reference equations were computed based on anthropometric measures, lung volumes, indices of the breathing pattern, and timing of breathing. By multi‐linear modeling reference equations of sReff, sGeff, and sWOB could be defined taking as independent parameters apart from anthropometric parameters, also parameters given by the ratio between the tidal volume and functional residual capacity (FRCpleth/V T), and the ratio between V T and inspiratory time (V T/T I). An alternative statistical approach to define reference equations of airway dynamics reveals that apart from the subject's anthropometric measurements, parameters of the magnitude of static lung volumes, the breathing pattern, and the timing of breathing are co‐variants of reference equations of airway dynamics over a large age range.

An alternative statistical approach to the collection and processing of Mössbauer spectroscopy data

The idea of recording the total history of a Mossbauer spectrum collection in carefully selected time intervals is introduced in this work, providing a potential to wield and interpret the mustered data using the novel-suggested experimental quantity of ‘count rate’, in comparison to the conventionally used experimental quantity of simple accumulated counts. The gain is a rich and detailed statistical set of data described by Poisson functions, that can be analyzed to reveal several characteristics, such as mean values and partial probability sums, providing different proposed experimental Mossbauer spectrum estimations. It is shown that the application of the proposed method affords improvements referring to line-width reduction and increase of signal to noise ratio to the resulting experimental 57Fe Mossbauer spectrum estimations, in comparison to the corresponding conventional spectrum recorded by simply accumulating counts. Further improvement of the experimental spectral resolution is accomplished through the implementation of a proposed background filtering algorithm, whose validity is moreover tested on theoretical Mossbauer spectra containing simulated Poisson noise, where it was also found to be effective. The method is proposed to have applications to other similar experimental techniques that are based on estimating statistical mean values of experimental physical quantities.

“One more time”: why replicating some syntheses of evidence relevant to COVID-19 makes sense

• Given the urgent need for credible answers to high-priority questions about the health and social impacts of COVID-19, many systematic reviewers seek to contribute their skills and expertise. • Rather than embarking on unnecessary, duplicate reviews, we encourage the evidence synthesis community to prioritise purposeful replication of systematic reviews of evidence relevant to COVID-19. • We explain why replication of systematic reviews is important, how to carry out a replication, and when to consider replication of reviews.

A comparison of the automated blood bank system IH-500 and manual tube method for anti-blood group antibody titration: a quantitative approach

BackgroundAnti-blood group antibody titers (ABTs) reported in titer values are variable depending on the testing method used. The introduction of new test methods such as automated methods requires proper method comparison. In this study, the automated blood bank system and manual tube method for ABT were compared using a log-transformed dataset to evaluate the alternative statistical approach. MethodsABT was conducted using specimens referred for solid organ transplantation. Methods for comparison were conventional manual tube method and IH-500 automated blood bank system using column agglutination (CAT). Criteria for agreement were exact match and 1-titer match. Measured titer values were log-transformed into interval scale for Deming regression analysis. ResultsFrom the comparison of the tube and CAT methods using titer values and the two criteria, the exact and 1-titer match were 15.9–41.5 % and 65.9–97.6 %, respectively. Deming regression was used to demonstrate the presence of both proportional and constant difference between the two methods. ConclusionThe method comparison using conventional statistical approaches had limits due to the semi-quantitative value of the test. Log-transformed interval scale values for comparison were useful for interpretation of method comparison datasets. This alternative statistical approach could contribute to a more accurate comparison between assays and standardization of ABT testing.

The thermal behaviour of French water bodies: From ponds to Lake Geneva

The management of French freshwater bodies (lakes, reservoirs, ponds, etc.) needs to face the challenges imposed by the effects of climate change and by legal requirements to develop standards for water temperature and other physicochemical indicators. While 1D hydrodynamical models could be helpful in improving the knowledge of the thermal and hydrodynamic behaviour of French water bodies, the scarcity of data for calibration and validation, in addition to the unavailability of some forcing data, makes their application difficult for most French water bodies. In this article we explore an alternative statistical approach that takes advantage of the available data in order to inform future modelling applications. We used official monitoring data and satellite measurements to study the thermal characteristics of more than 400 French water bodies (depth: 1–310 m, surface area: 0.1–577 km2, volume: 0.1–89,000 hm3). The objective was to identify the importance of size on their thermal behaviour. For this we analysed the annual temperature cycle, the summer temperature profiles and within-lake spatial variability. Together with size, geography and transparency determine the thermal behaviour of water bodies through complex interactions. As a result of the analysis we propose a classification of French water bodies as a function of depth and surface area that reflects the summer vertical temperature gradient and the processes influencing the deepening of the thermocline.

On the Statistical Significance of Foreshock Sequences in Southern California

AbstractEarthquake foreshocks may provide information that is critical to short‐term earthquake forecasting. However, foreshocks are far from ubiquitously observed, which makes the interpretation of ongoing seismic sequences problematic. Based on a statistical analysis, Trugman and Ross (2019, https://doi.org/10.1029/2019GL083725) suggested that as much as 72% of all mainshocks in Southern California is preceded by foreshock sequences. In this study, we reassess the analysis of Trugman and Ross (2019, https://doi.org/10.1029/2019GL083725), and we evaluate the impact of the assumptions made by these authors. Using an alternative statistical approach, we find that only 15 out of 46 mainshocks (33%) are preceded by significantly elevated seismicity rates. When accounting for temporal fluctuations in the background seismicity, only 18% of the analyzed foreshock sequences remain unexplained by the background seismicity. These results imply that even in a highly complete earthquake catalog, the majority of earthquakes do not exhibit detectable foreshock sequences.

Mass Spectrometry Characterization of Higher Order Structural Changes Associated with the Fc-glycan Structure of the NISTmAb Reference Material, RM 8761.

As monoclonal antibodies (mAbs) rapidly emerge as a dominant class of therapeutics, so does the need for suitable analytical technologies to monitor for changes in protein higher order structure (HOS) of these biomolecules. Reference materials (RM) serve a key analytical purpose of benchmarking the suitability and robustness of both established and emerging analytical procedures for both drug producers and regulators. Here, two simple enzymatic protocols for generating Fc-glycan variants from the NISTmAb RM are described and both global and localized changes in HOS between the RM and these Fc-glycan variants are characterized using hydrogen deuterium exchange-mass spectrometry (HDX-MS) and ion mobility spectrometry-mass spectrometry (IMS-MS) measurements. An alternative statistical approach is described where measurement thresholds that differentiate between measurement variability and significant structural changes were established on the basis of experimental data. Measurements revealed decreases in structural stability correlating with the degree of Fc-glycan structure loss, especially at the CH2/CH3 domain interface. These data promote the use of this RM and these Fc-glycan variants for establishing the sensitivity of and validating analytical methods for the detection of HOS measurements of mAbs.

Analysis of the Space–Temporal Trends of Wet Conditions in the Different Rainy Seasons of Brazilian Northeast by Quantile Regression and Bootstrap Test

Drought causes serious social and environmental problems that have great impact on the lives of thousands of people all around the world. The purpose of this research was to investigate the trends in humid conditions in the northeast of Brazil (NEB) in the highest climatic precipitation quarters, November–December–January (NDJ), February–March–April (FMA), and May–June–July (MJJ), through the standardized precipitation and evapotranspiration index (SPEI), considering an alternative statistical approach. Precipitation and potential evapotranspiration (PET) time series for the calculation of the SPEI were extracted for the 1794 NEB municipalities between 1980 and 2015 from a grid dataset with a resolution of 0.25° × 0.25° using the bilinear interpolation method. The trends and statistical significance of the SPEI were estimated by quantile regression (QR) and the bootstrap test. In NDJ, opposite trends were seen in the eastern NEB (~0.5 SPEI/decade) and in the south (~−0.6 SPEI/decade). In FMA, most of NEB presented negative trends in the 0.50 and 0.95 quantiles (~−0.3 SPEI/decade), while in MJJ, most of NEB presented positive trends in all quantiles studied (~0.4 SPEI/decade). The results are consistent with observational analyses of extreme rainfall.

Rethinking segmentation within the psychological continuum model using Bayesian analysis

The Psychological Continuum Model (PCM) represents a theoretical framework in sport management to understand why and how consumer attitudes form and change. Prior researchers developed an algorithmic staging procedure using psychological involvement to operationalize the PCM framework within sport and recreational contexts. Although this staging procedure is pragmatically sound, it rests upon a procedure that, while intuitively sensible, lacks scientific rigor. The current research offers an alternative approach to PCM segmentation using Bayesian Latent Profile Analysis (Bayesian LPA). Comparing three analyses (the conventional PCM segmentation algorithm, K-means clustering, and Bayesian LPA), results demonstrated that Bayesian LPA provides a promising and alternative statistical approach that outperforms the conventional PCM staging algorithm in two ways: (a) it has the ability to classify individuals into the corresponding PCM segments with more distinct boundaries; and (b) it is equipped with stronger statistical power to predict conceptually related distal outcomes with larger effect size.

Entropy Analysis of High-Definition Transcranial Electric Stimulation Effects on EEG Dynamics.

A foundation of medical research is time series analysis—the behavior of variables of interest with respect to time. Time series data are often analyzed using the mean, with statistical tests applied to mean differences, and has the assumption that data are stationary. Although widely practiced, this method has limitations. Here we present an alternative statistical approach with sample analysis that provides a summary statistic accounting for the non-stationary nature of time series data. This work discusses the use of entropy as a measurement of the complexity of time series, in the context of Neuroscience, due to the non-stationary characteristic of the data. To elucidate our argument, we conducted entropy analysis on a sample of electroencephalographic (EEG) data from an interventional study using non-invasive electrical brain stimulation. We demonstrated that entropy analysis could identify intervention-related change in EEG data, supporting that entropy can be a useful “summary” statistic in non-linear dynamical systems.

Analysis of the statistics of device-to-device and cycle-to-cycle variability in TiN/Ti/Al:HfO2/TiN RRAMs

In order to study the device-to-device and cycle-to-cycle variability of switching voltages in 4-kbit RRAM arrays, an alternative statistical approach has been adopted by using experimental data collected from a batch of 128 devices switched along 200 cycles. The statistical distributions of switching voltages have been usually studied by using the Weibull distribution. However, this distribution does not work accurately on Al:HfO2-based RRAM devices. Therefore, an alternative approach based on phase-type distributions is proposed to model the forming, reset and set voltage distributions. Experimental results show that in general the phase-type analysis works better than the Weibull one.