Principal Component Regression Research Articles

Day‐To‐Day Variability of the Neutral Wind Dynamo Observed by ICON: First Results From Conjugate Observations

AbstractFirst results are presented from the conjugate maneuvers performed by NASA's Ionospheric Connection Explorer (ICON) spacecraft. During each several‐minute maneuver, ICON crosses the magnetic equator, measuring the plasma drift at the ∼600‐km apex of a magnetic field line and the neutral wind profiles (∼90–300 km altitude) along both ends of that field line. The analysis utilizes 149 pairs of maneuvers separated by ∼24 hr but at nearly the same location and local time. Principal component regression reveals that 39 ± 7% and 24 ± 9% of the day‐to‐day variance in the daytime vertical and zonal drift, respectively, is attributable to conjugate neutral winds. The remaining variance is likely driven by external potentials from non‐conjugate winds and geomagnetic activity (median Kp 2−). Zonal winds at 100–113 km and >120 km altitude are the primary drivers of conjugate vertical and zonal drift variance, respectively. These observations can test vertical‐coupling mechanisms in whole‐atmosphere models.

The Asymptotic Equivalence of Ridge and Principal Component Regression with Many Predictors

The asymptotic properties of ridge regression in large dimension are studied. Two key results are established. First, consistency and rates of convergence for ridge regression are obtained under assumptions which impose different rates of increase in the dimension n between the first n1 and the remaining n−n1 eigenvalues of the population covariance of the predictors. Second, it is proved that under the special and more restrictive case of an approximate factor structure, principal component and ridge regression have the same rate of convergence and the rate is faster than the one previously established for ridge.

Merging two cultures: Deep and statistical learning

AbstractOur goal is to provide a review of deep learning methods which provide insight into structured high‐dimensional data. Merging the two cultures of algorithmic and statistical learning sheds light on model construction and improved prediction and inference, leveraging the duality and trade‐off between the two. Prediction, interpolation, and uncertainty quantification can be achieved using probabilistic methods at the output layer of the model. Rather than using shallow additive architectures common to most statistical models, deep learning uses layers of semi‐affine input transformations to provide a predictive rule. Applying these layers of transformations leads to a set of attributes (or, features) to which probabilistic statistical methods can be applied. Thus, the best of both worlds can be achieved: scalable prediction rules fortified with uncertainty quantification where sparse regularization finds the features. We review the duality between shallow and wide models such as principal components regression, and partial least squares and deep but skinny architectures such as autoencoders, multilayer perceptrons, convolutional neural net, and recurrent neural net. The connection with data transformations is of practical importance for finding good network architectures. By incorporating probabilistic components at the output level, the predictive uncertainty is allowed. We illustrate this idea by comparing plain Gaussian processes (GP) with partial least squares + Gaussian process (PLS + GP) and deep learning + Gaussian process (DL + GP).This article is categorized under: Statistical Learning and Exploratory Methods of the Data Sciences > Deep Learning

Thermal error modelling of gear measuring instrument based on principal component regression

As gear measuring instruments (GMI) are frequently used at the production site, the influence of temperature on their accuracy and stability becomes non-negligible. The existing standards and technologies only consider the error modelling and compensation problem when the GMI is in a uniform temperature field and is not suitable for the production site environment with a large temperature change rate and large temperature gradient. The paper delves into the problems of GMI thermal error modelling at the production site. First, the temperatures of the GMI and its surroundings were measured using multiple temperature sensors. The parallelism thermal errors between centres and the Z-axis (referred to as parallelism thermal errors) and the y-direction spatial thermal errors (referred to as spatial thermal errors) were also measured by the probe and laser interferometer synchronously. Then, the models of them were established based on principle component regression (PCR). As evidenced by the experimental results, the fitting determination coefficient of the models are both greater than 97%, and the prediction determination coefficient is greater than 90%, demonstrating that the models are precise and robust.

Analisis Regresi Komponen Utama untuk Mengatasi Multikolinearitas pada Faktor-Faktor yang Mempengaruhi Indeks Pembangunan Manusia

West Nusa Tenggara and East Nusa Tenggara provinces are among the provinces with low human development indexes. There are seven factors used in this study that are considered to affect the human development index in the two provinces, namely gross regional domestic product, poor population, open unemployment rate, population, life expectancy, labor force and average years of schooling. The method used by researchers in overcoming multicollinearity in this study is principal component regression. Therefore, this study aims to apply principal component regression in overcoming the problem of multicollinearity on the the effect of gross regional domestic product, poor population, open unemployment rate, population, life expectancy, labor force and average years of schooling on the human development index. Based on the results of the analysis that has been carried out, the principle component regression model is obtained as follows Y = 42.548 + 0.00000991X_1 + 0.0371X_2 + 0.343X_3 + 0.000005949X_4 + 0.2532 + 0.000012947X_6 + 0.1348X_7. With the coefficient of determination (R^2) 0.834.

The Relationship Between Shift Work Patterns and Cognitive Function Disorders in Health Workers at Hospital X, West Java, Indonesia

Introduction: Jobs with shift work patterns, especially irregular ones, can disrupt circadian rhythms and sleep quality, which then impacts cognitive function. Despite its importance, research on cognitive domain vulnerabilities related to shift work patterns is still limited. Sleep quality and cognitive function are critical in the context of hospital health services, where decisions and actions must be taken quickly and precisely to support patient safety.
 Methods: This research is a cross-sectional observational analytical study to determine the relationship between shift work patterns and impaired cognitive function. To measure sleep quality, the Pittsburgh Sleep Quality Index Indonesian (PSQI-Ina) is used, while cognitive function and cognitive domains were measured using Oxford Cognitive Screen (OCS) Indonesian (OCS-Ina), a cognitive instrument for stroke patients, which has been validated. The minimum sample size in this study was 72 samples. Correlation, principal component analysis, demographic analysis, and regression were used to characterize the relationship between PSQI-Ina, OCS-Ina, and other research variables.
 Results: A total of 83 health workers met the inclusion criteria and were included in the research. The results showed that 16 respondents (19.3%) experienced impaired cognitive function in the Attention domain and 2 respondents (2.4%) experienced impairment in 2 cognitive domains (Attention and Number Management). The results of statistical analysis showed a significant relationship with health workers who had part-time jobs having a 12.8-fold risk (OR 12.8; 95% CI 1.7-91; p = 0.011) of experiencing cognitive impairment. Then health workers who experience poor sleep quality (with a PSQI value >5) have a 40.3 times greater risk (OR 40.3; 95% CI 2.2-708.1; p = 0.011) of experiencing cognitive impairment. Likewise, health workers with irregular shift work patterns have a 5.4 times risk of experiencing cognitive impairment (OR 5.4; 95% CI 0.1-26.6; p = 0.036).
 Conclusion: There is a relationship between shift work patterns and impaired cognitive function in the workplace. Hospitals should prioritize ergonomic shift work schedules, emphasizing speed and clockwise rotation, to support the well-being of their healthcare workers.

TV뉴스 정보특성이 관광동기, 여행만족도, 행동의도에 미치는 영향 연구

Purpose: TV news contents spread out via various media instantly and simultaneously. Focusing on those who are exposed to TV news contents on Gangneung, the city of cultural tourism, this study aims to identify the impact of TV news contents on travel motivation (i.e., intrinsic (PUSH) and the destination attraction factors (PULL)), travel satisfaction, and behavioral intention
 Methods: Using quota sampling, 656 data were collected from Gangneung visitors. Several statistical analyses were conducted using SPSS 28.0, including frequency analysis, reliability analysis, principal component analysis, correlation analysis, and regression analysis.
 Results: The results revealed the following key findings. Firstly, among the factors of timeliness, reliability, and usefulness, the usefulness factor showed the most significant influence in both PUSH and PULL factors, indicating that positive or negative news coverage about the region alters travelers' destination selection and decision-making. Secondly, among the PUSH and PULL factors, sociability, pleasure-seeking, natural scenery, and convenience had a significant impact on travel satisfaction, while escapism and entertainment did not affect travel satisfaction. Thirdly, travel satisfaction significantly influenced the intention to revisit and TV news viewing intention. Lastly, timeliness and usefulness of TV news had a significant impact on viewing intention, but reliability did not show such influence.
 Conclusion: Positive or negative news coverage about the region in TV news affects the development of the local tourism industry. In case of small local cities with a weak financial independency, the utilization of news media is an effective avenue to tourism promotion.

Towards variance-conserving reconstructions of climate indices with Gaussian process regression in an embedding space

Abstract. We present a new framework for the reconstruction of climate indices based on proxy data such as tree rings. The framework is based on the supervised learning method Gaussian Process Regression (GPR) and aims at preserving the amplitude of past climate variability. It can adequately handle noise-contaminated proxies and variable proxy availability over time. To this end, the GPR is formulated in a modified input space, termed here embedding space. We test the new framework for the reconstruction of the Atlantic multi-decadal variability (AMV) in a controlled environment with pseudo-proxies derived from coupled climate-model simulations. In this test environment, the GPR outperforms benchmark reconstructions based on multi-linear principal component regression. On AMV-relevant timescales, i.e. multi-decadal, the GPR is able to reconstruct the true amplitude of variability even if the proxies contain a realistic non-climatic noise signal and become sparser back in time. Thus, we conclude that the embedded GPR framework is a highly promising tool for climate-index reconstructions.

Risk governance and regulatory adjustments in the public commercial banks of OECD

PurposeThis study aims to explore the relationship between risk governance characteristics (chief risk officer [CRO], chief financial officer [CFO] and senior directors [SENIOR]) and regulatory adjustments (RAs) in Organization for Economic Cooperation and Development public commercial banks.Design/methodology/approachUsing principal component analysis (PCA) and regression models, the research analyzes a representative data set of these banks.FindingsA significant negative correlation between risk governance characteristics and RAs is found. Sensitivity analysis on the regulatory Tier 1 capital ratio and the total capital ratio indicates mixed outcomes, suggesting a complex relationship that warrants further exploration.Research limitations/implicationsThe study’s limited sample size calls for further research to confirm findings and explore risk governance’s impact on banks’ capital structures.Practical implicationsEnhanced risk governance could reduce RAs, influencing banking policy.Social implicationsThe study advocates for improved banking regulatory practices, potentially increasing sector stability and public trust.Originality/valueThis study contributes to understanding risk governance’s role in regulatory compliance, offering insights for policymaking in banking.

Social poverty indicators with school bullying victimization: evidence from the global school-based student health survey (GSHS)

BackgroundSchool bullying is prevalent in children and adolescents. Bullying victims are seen higher risk of negative psychological outcomes. Previously published studies suggested that social indicators may pose significant influence on bullying victimization. However, the association between social poverty and bullying victimization has not been exclusively discussed.MethodsIn this cross-sectional study, we analyzed the association between 6 commonly used social poverty indicators (Poverty Headcount Ratio, PHR; Poverty Gap, PG; Squared Poverty Gap, SPG; monthly household per capita income, PCI; Watts’ Poverty Index, WPI; the Gini Index, Gini) and the prevalence of school bullying at country level by using the Global school-based Student Health Survey (GSHS) database.ResultsAltogether 16 countries were included into the final analysis, with school bullying victimization prevalence ranged from 12.9 to 47.5%. Bubble plots revealed statistically significant associations between the three indicators measuring absolute poverty level (PHR, PCI, WPI) and bullying victimization. Subsequently performed principal component regression indicated that, for all types of bullying victimization, the increase of absolute poverty level was related to elevated prevalence rates, and the association was particularly strong for verbal bullying victimization.ConclusionsOur study results may suggest that absolute social poverty is an important parameter for constructing and implementing school bullying victimization intervention strategies and measures.

The Positive Association between Grit and Mental Toughness, Enhanced by a Minimum of 75 Minutes of Moderate-to-Vigorous Physical Activity, among US Students

Drawing from the 2015 Gucciardi et al.’s mental toughness (MT) framework, this study examines the association between grit and MT in US college students, while considering the moderating role of at least 75 min of moderate-to-vigorous physical activity (MVPA) based on recommendations from the American College of Sports Medicine. We administered the Grit-S Scale and the Mental Toughness Index in two samples of a total of 340 US undergraduate student-athletes and graduate students. The Quality Assessment Tool for Observational Cohort and Cross-Sectional Studies was employed to ensure internal validity, while statistical procedures including principal component analysis and regression models were utilized to analyze the collected data. A weighted component combining grit and the interaction between MVPA and grit significantly predicted MT, explaining 23% of its variability. Drawing from a specific conceptual framework, this study provides novel insights into the relationship between grit, engagement in at least 75 min of MVPA per week, and MT among US collegiate students. The findings support a positive association between grit, MVPA, and both MT and a specific component of MT, highlighting the significance of these factors in enhancing performance and suggesting potential implications for future research and practical applications in the field.

Changes in arterial pH do not explain the reductions in ionised calcium observed during COVID-19 infection

BackgroundHypocalcaemia predicts coronavirus disease 2019 (COVID-19) severity and mortality. We hypothesized an association between respiratory alkalosis secondary to hypoxia and low ionised calcium (iCa) concentration in patients with COVID-19. MethodsArterial blood gas samples taken from January 2019 to March 2021 were retrospectively matched with infection status. Principal components regression was undertaken to determine the correlation between pH, partial pressure arterial oxygen (PaO2), partial pressure arterial carbon dioxide (PaCO2), and iCa. ResultsWe included 4056 patients (300 COVID-19 detected, 19 influenza detected), corresponding to 5960 arterial blood samples. The COVID-19 detected group had a statistically significantly lower iCa, PaO2 and PaCO2, and more alkalotic pH than infection-free groups. The influenza group had a lower iCa and PaCO2, higher PaO2, and a more alkalotic pH than infection-free groups, but these differences were non-significant. Principal components regression revealed that pH, PaCO2, and PaO2 explain just 2.72 % of the variance in iCa. An increase in pH by 1 unit was associated with an iCa reduction of 0.141 ​mmol/L (p ​< ​0.0001). ConclusionReduction in iCa concentration in patients with COVID-19 is not associated with pH derangement. Influenza infection was associated with a minor reduction in iCa in our small sample, a hitherto unreported finding, although statistical significance was not demonstrated.

Modeling of flaxseed protein, oil content, linoleic acid, and lignan content prediction based on hyperspectral imaging.

Protein, oil content, linoleic acid, and lignan are several key indicators for evaluating the quality of flaxseed. In order to optimize the testing methods for flaxseed's nutritional quality and enhance the efficiency of screening high-quality flax germplasm resources, we selected 30 flaxseed species widely cultivated in Northwest China as the subjects of our study. Firstly, we gathered hyperspectral information regarding the seeds, along with data on protein, oil content, linoleic acid, and lignan, and utilized the SPXY algorithm to classify the sample set. Subsequently, the spectral data underwent seven distinct preprocessing methods, revealing that the PLSR model exhibited superior performance after being processed with the SG smoothing method. Feature wavelength extraction was carried out using the Successive Projections Algorithm (SPA) and the Competitive Adaptive Reweighted Sampling (CARS). Finally, four quantitative analysis models, namely Partial Least Squares Regression (PLSR), Support Vector Regression (SVR), Multiple Linear Regression (MLR), and Principal Component Regression (PCR), were individually established. Experimental results demonstrated that among all the models for predicting protein content, the SG-CARS-MLR model predicted the best, with and of 0.9563 and 0.9336, with the corresponding Root Mean Square Error Correction (RMSEC) and Root Mean Square Error Prediction (RMSEP) of 0.4892 and 0.5616, respectively. In the optimal prediction models for oil content, linoleic acid and lignan, the was 0.8565, 0.8028, 0.9343, and the RMSEP was 0.8682, 0.5404, 0.5384, respectively. The study results show that hyperspectral imaging technology has excellent potential for application in the detection of quality characteristics of flaxseed and provides a new option for the future non-destructive testing of the nutritional quality of flaxseed.

Identifying wine grape aromatic maturity using e-nose and GC-MS: the case of Nerello Mascalese grapes from two contrade of the Etna area

A study of aromatic maturity of Nerello Mascalese grapes from two districts (“Contrade”) of the Etna area was carried out using gas chromatography-mass spectrometry (GC-MS) and electronic nose (E-nose). To validate our hypothesis regarding the potential use of E-nose for aromatic maturity, two vineyards with different characteristics (08 Alto and Solicchiata) were used. Grapes were sampled at 18°, 21° and 23°Brix. Regarding the phenol maturity index, total anthocyanins reached a peak at the second sampling in 08 Alto grapes, while in Solicchiata they constantly increased. The ratio total anthocyanins: total polyphenols in 08 Alto grapes increased from 0.14 to 0.33, and in Solicchiata from 0.17 to 0.23. As regards grape volatile organic compounds (GVOCs) for the aromatic maturity index, in Solicchiata the concentrations of glycosylated benzenoids and C13-norisoprenoids were much higher than in 08 Alto, and the concentration decreased during maturity (opposite trend to the anthocyanins); by contrast, in 08 Alto, concentrations peaked at the second sampling time (as with the anthocyanins). The E-nose results did not completely coincide with the GVOCs pattern, but they discriminated the maturity stages very well. However, the different metallo-porphyrins responded differently depending on the class of GVOCs, highlighting very promising results in terms of GVOCs non-destructive prediction by means of principal component regression (PCR) application. E-nose shows potential for easy use with rapid PCR for the monitoring of the aromatic maturity of Nerello Mascalese grapes.

Bootstrapping Long-Run Covariance of Stationary Functional Time Series

A key summary statistic in a stationary functional time series is the long-run covariance function that measures serial dependence. It can be consistently estimated via a kernel sandwich estimator, which is the core of dynamic functional principal component regression for forecasting functional time series. To measure the uncertainty of the long-run covariance estimation, we consider sieve and functional autoregressive (FAR) bootstrap methods to generate pseudo-functional time series and study variability associated with the long-run covariance. The sieve bootstrap method is nonparametric (i.e., model-free), while the FAR bootstrap method is semi-parametric. The sieve bootstrap method relies on functional principal component analysis to decompose a functional time series into a set of estimated functional principal components and their associated scores. The scores can be bootstrapped via a vector autoregressive representation. The bootstrapped functional time series are obtained by multiplying the bootstrapped scores by the estimated functional principal components. The FAR bootstrap method relies on the FAR of order 1 to model the conditional mean of a functional time series, while residual functions can be bootstrapped via independent and identically distributed resampling. Through a series of Monte Carlo simulations, we evaluate and compare the finite-sample accuracy between the sieve and FAR bootstrap methods for quantifying the estimation uncertainty of the long-run covariance of a stationary functional time series.

The Chemometric Approach for Verification of Paracetamol Level in Pharmacies Products Using Spectrophotometer UV-Vis

The objective of this study is to inspect paracetamol levels in Indonesian pharmacies and whether the level of paracetamol is matched the label given. The verification of paracetamol level using a chemometric approach such as partial least squares (PLS) and principal component regression (PCR) after measured with spectrophotometer UV. The outcome of this study showed the absorbance at 242 with an R2 (0.9991) and the linear range at 1.0 – 6.0 μg·mL-1. The accuracy was satisfactory and obtained at 99.18%. The satisfactory outcomes revealed the identification method has a good potential to be used in inspecting the paracetamol level in pharmaceutical products. Furthermore, the chemometric method used as a statistical measurement showed that there was no specific distinction to the validated approach. Thus, this approach can be applied in pharmacy industries specifically for small industries to ensure the medicine's safety before being distributed to the pharmacies.

How much can performance measures explain of the between-cow variation in enteric methane?

Enteric CH4 produced from dairy cows contributes to the greenhouse gas emission from anthropogenic sources. Recent studies have shown that the selection of lower CH4 emitting cows is possible, but this would be simpler if performance measures already recorded on farm could be used, instead of measuring gas emission from individual cows. These performance measures could be used for selection of low emitting cows. The aim of this analysis was to quantify how much of the between-cow variation in CH4 production can be explained by variation in performance measures. A data set with 3 experiments, a total of 149 lactating dairy cows with repeated measures, was used to estimate the between-cow variation (the variation between cow estimates) for performance and gas measures from GreenFeed. The cow estimates were obtained with a linear mixed model with the diet within period effect as a fixed effect and the cow within experiment as a random effect. The cow estimates for CH4 production were first regressed on the performance and gas measures individually, and then performance and CO2 production measures were grouped in 3 subsets for principal component analysis and principal component regression. The variables that explained most of the between-cow variation in CH4 production were DMI (R2 = 0.44), among the performance measures, and CO2 production (R2 = 0.61), among gas measures. Grouping the measures increased the R2 to 0.53, when only performance measures were used, and to 0.66, when CO2 production was added to the significant performance measures. We found the marginal improvement to be insufficient to justify the use of grouped measures rather than an individual measure, since the latter avoid over fitting and simplify the model. Investigation of other measures that can be explored to increase explanatory power of between-cow variation in CH4 production is briefly discussed. Finally, the use of residual CH4 as a measure for CH4 efficiency could be considered by using either DMI or CO2 production as the sole predicting variables.

Evaluating the Usefulness of Least Squares Regression in Petrophysics Interpretation

This paper is to update the information provided in the author’s previous papers on evaluating the uncertainty in least squares results. It is prompted by new information that shows that the usefulness of any least squares result cannot be guaranteed by conventional statistics (such as R-squared, F-statistic, or standard error of the regression, sigma). A new method based on the singular value decomposition (SVD) of a matrix, when accompanied by a Relative Error Bound (REB) on the estimated parameters, provides the user with a tool that can better assess the usefulness of any least squares result. Another important aspect of the REB is that it provides the user of the SVD method with a powerful tool for judging which is the best among several candidate solutions. In addition, it provides the user with a numerically stable method of computing the data ordinarily provided by principal component regression by eliminating the need to perform an eigenvector-eigenvalue analysis of an ATA matrix. This is of particular interest because forming the ATA matrix is often accompanied by a loss of data. The new method also provides the user with an improved method for selection of which principal components should be retained for a given problem.

Interpreting Image Patterns for Agricultural Sprays Using Statistics and Machine Learning Techniques

The atomization of liquid spray solutions through nozzles is a mechanism for delivering many pesticides to the target. The smallest drop sizes (&lt;150 μm) are known as driftable fines and have a propensity for wind-induced convection. Many agricultural applications include oil-in-water formulations. The experimental metrics obtained from spray images of these formulations include the distance from the nozzle origin to the drop centroid once a drop has formed; the hole location and surface area for holes that form in the liquid sheet (all hole areas approximated as polygons); the angles formed between polygon segments (whose vertices are represented as boundary points); and the ligament dimensions that form from intersecting holes, such as the ligament aspect ratio (R/L), ligament length (L), and ligament radius (width), along with the number of drops a ligament breaks up into. These metrics were used in a principal component regression (PCR) analysis, and the results illustrated that 99% of the variability in the response variable (DT10) was addressed by 10 principal components. Angles formed by the colliding holes, hole distance from the nozzle, drop distance, hole number, ligament number, and drop number were negatively correlated to the atomization driftable fine fraction, while hole area, ligament distance, ligament area, and boundary area were positively correlated. Thus, to decrease/minimize driftable fines, one needs to increase the negatively correlated metrics.

Transformations for non-destructive evaluation of brix in mango by reflectance spectroscopy and machine learning

Mango is a very popular climacteric fruit in America and Europe. Among the internal properties of the mango, total soluble solids (TSS) are an adequate indicator to estimate the quality of mango, however, the measurement of this indicator requires destructive tests. Several research have addressed similar issues; they have made use of pre-processing transformations without making it clear which of them is statistically better. Here, we created a new spectral database to build machine learning (ML) models. We analyzed a total of 18 principal component regression (PCR) models and 18 partial least squared regression (PLSR) models, where 4 types of transformations, 3 different feature extractors, and 3 different pre-processing techniques are combined. The research proposes a double cross validation (CV) both to determine the optimal number of components and to obtain the final metrics. The best model had a root mean square error (RMSE) of 1.1382 °Brix and a RMSE on the transformed scale of 0.5140. The best model used 4 components, used y&lt;sup&gt;2&lt;/sup&gt; transformation, reflectance R as the independent variable and MSC as a pre-processing technique.