Principal Component Approach Research Articles

Coherent mortality forecasting by the weighted multilevel functional principal component approach

ABSTRACTIn human mortality modelling, if a population consists of several subpopulations it can be desirable to model their mortality rates simultaneously while taking into account the heterogeneity among them. The mortality forecasting methods tend to result in divergent forecasts for subpopulations when independence is assumed. However, under closely related social, economic and biological backgrounds, mortality patterns of these subpopulations are expected to be non-divergent in the future. In this article, we propose a new method for coherent modelling and forecasting of mortality rates for multiple subpopulations, in the sense of nondivergent life expectancy among subpopulations. The mortality rates of subpopulations are treated as multilevel functional data and a weighted multilevel functional principal component (wMFPCA) approach is proposed to model and forecast them. The proposed model is applied to sex-specific data for nine developed countries, and the results show that, in terms of overall forecasting accuracy, the model outperforms the independent model and the Product-Ratio model as well as the unweighted multilevel functional principal component approach.

Analysis of Nigeria's Crime Data: A Principal Component Approach using correlation matrix

Analysis of Nigeria's Crime Data: A Principal Component Approach using correlation matrix

Quantification of 27 yolk steroid hormones in seven shrubland bird species: interspecific patterns of hormone deposition and links to life history, development, and predation risk

Steroid hormones play critical organizational and activational roles during vertebrate development, impacting everything from sexual differentiation to metabolic activity. For oviparous species such as birds, these hormones are transferred from female to egg during follicle maturation, and differences in relative and absolute concentrations of the steroid hormones may reflect differences in life history, developmental, and ecological conditions. Prior work on yolk steroid hormones has focused on a handful of candidate hormones (e.g., testosterone, androstenedione, and corticosterone), but we used high-performance liquid chromatography with tandem mass spectroscopy (LC–MS–MS) to quantify 27 yolk steroids from the eggs of seven shrubland bird species (American Robin, Turdus migratorius Linnaeus, 1766; Brown-headed Cowbird, Molothrus ater (Boddaert, 1783); Brown Thrasher, Toxostoma rufum (Linnaeus, 1758); Eastern Towhee, Pipilo erythrophthalmus (Linnaeus, 1758); Field Sparrow, Spizella pusilla (A. Wilson, 1810); Gray Catbird, Dumetella carolinensis (Linnaeus, 1766); Northern Cardinal, Cardinalis cardinalis (Linnaeus, 1758)). In addition to comparing steroid profiles across species, we conducted exploratory analyses to determine how the hormones clustered using a principal component (PC) approach and if PCs were correlated with aspects of egg resources (relative egg size, proportion yolk), life-history traits (embryonic and nestling development speed), and nest-predation risk (daily survival rate (DSR)). We documented substantial interspecific variation in both absolute and proportional endocrine profiles. PCAs indicated that glucocorticoids generally clustered together (PC1), but other classes of steroids did not. PC2 and PC3 strongly covaried with egg resources, DSR, and development speed, suggesting that they reflect adaptive patterns of maternal hormone deposition.

Regional disparities in Gujarat dairy development - Principal component approach

Growing regional inequalities is a major concern for policy makers on developmental front. The present study reveals the regional disparities in dairy development of Gujarat which is considered as a role model for dairy industry in India. The relative development of 26 districts in Gujarat was measured by a composite dairy development Index (DDI) using 18 different indicators. Variables related to milk production, availability of resources, infrastructure facilities and dairy cooperatives were considered for the study. Holding linearity relationship assumption, principal component analysis was employed to develop the DDI. As per the index score, Anand, Banaskantha, Mehsana, Sabarkantha, Gandhinagar, Patan and Jamnagar were grouped as highly developed. Kheda, Rajkot, Porbandar, Junagadh, Amreli, Bhavnagar, Kachh, Ahmedabad, Surat and Bharuch were categorized as moderately developed while Dang, Dahod, Narmada, Panchmahal, Surendranagar, Navsari, Vadodara, Tapi and Valsad were placed in low dairy developed districts. The study reveals that the strength of dairy cooperative structure in terms of its functions and bovine breed spread can be accounted as prime factors for regional dairy development disparity. Low developed districts lacked in high quality bovine breeds and poor cooperative structure, which needs policy focus. Elimination of the regional disparities could pave way towards the balanced and sustained growth and development of the dairy sector.

Understanding Visitor Motivations at Jimmy Carter National Historic Site: A Principal Components Approach

National park sites draw tourism all across the United States. Although large natural parks see much attention, most national park units are actually designed to protect and interpret unique cultural and historic resources. As an example of this, the National Park Service administers numerous presidential historic sites. However, we know very little about the people who visit them. Understanding visitor motivations to presidential historic sites can help to provide for better visitor experiences of presidential resources. This research uses intercept surveys at the Jimmy Carter National Historic Site in Plains, Georgia, to gain an understanding of visitor motivations. From the results, seven motivation types are identified. The information in this article can be used to better understand public values related to presidential resources, and to help the managers of these resources to improve on-site experiences by addressing visitor motivations.

Cultural influences on the processing of social comparison feedback signals-an ERP study.

This study investigated cultural differences regarding social connectedness in association with social vs non-social comparison feedback. We performed electroencephalography in 54 Chinese and 49 Western adults while they performed a time estimation task in which response–accuracy feedback was either delivered pertaining to participants’ own performance (non-social reference frame) or to the performance of a reference group (social reference frame). Trait interdependence and independence were assessed using a cultural orientations questionnaire. Applying a principal component approach, we observed divergent effects for the two cultural groups during feedback processing. In particular, Feedback-Related Negativity results indicated that non-social (vs social) reference feedback was more salient/motivating for Chinese participants, while Westerners showed the opposite pattern. The results suggest that Chinese individuals perceive a non-social context as more salient than a social comparison context, possibly due to their extensive experience of social comparisons in daily life. The reverse pattern was found in Western participants, for whom a social comparison context is less common and presumably more salient. The cultural differences in neural responses to social vs non-social feedback might be caused by culturally diverse cognitive traits, as well as by exposure to culturally defined behaviour on a systemic level—such as the education system.

Psychometric Evaluation of the Short Form of Situational Temptation Scale for Smoking Cessation among Iranian Workers Based on Transtheoretical Model

Background: Situational temptations are important key points for smoking cessation. However, temptation to engage in smoking across different situations is yet challenging. In Iran, there is no measurement to assess situational temptations among workers. Objectives: The current study aimed at psychometrically evaluating the short form of situational temptations scale for smoking cessation based on transtheoretical model. Methods: By stratified random sampling method, 218 male current smoking automobile factory workers were selected and assessed through the short form of situational temptation scale. Exploratory factor analysis (EFA), confirmatory factor analysis (CFA), and Cronbach’s alpha were applied. Results: The mean age of the workers was 30.52 ± 6.66 years. CVI and CVR of each question were > 0.71. The result of EFA with principal component approach showed 1 factor with 45% cumulative variance and KMO was 91%, which were the good fit index in CFA. In the area of CFA, the result showed REMSEA = 0.006, GFI = 0.973, AGFI = 0.955; P value = 0.452, which were the good fit index in CFA. The reliability was also confirmed with Cronbach’s alpha (α = 80.1%). Conclusions: The current study approved the short form of situational temptation scale for smoke cessation. However, to ensure a reliable/valid instrument to realize smoking behaviors, doing more researches are recommended.

Cash Flow Risk Management in the Property/Liability Insurance Industry: A Dynamic Factor Modeling Approach

This study proposes and demonstrates a dynamic factor model that can be empirically carried out by the utilization of a factor-augmented autoregressive technique to explain and forecast the time-varying patterns of cash flows of insurance companies in the United States. A principal component approach is employed in the Factor-Augmented Autoregressive Model (FAARM) to capture the augmented factors that are to be utilized for forecasting. We describe the cash flow statistical model by a dimension-reduction technique that can depict the dynamic patterns of the cash flows of insurance firms and then measure the FAARM model. Results from the first step (principal component analysis) help capture the macroeconomic variables and the variables pertaining to insurance companies' cash flows, namely, cash flows from investment, underwriting, and risk management activities. Results from the second step offer evidence supporting that the FAARM improves the out-of-sample forecasting accuracy assessed by a forecasted root-mean-squared error (FRMSE). This article presents a set of feasible FAAR models from which an insurance firm can choose one that can be a better fit to the firm corresponding to its specific firm characteristics, such as firm size. Consequently, the chosen FAARM(s) can improve the accuracy of cash flow forecasting and thus can help insurers to manage risk via cash-flow–matching techniques.

Levodopa-Induced Changes in Electromyographic Patterns in Patients with Advanced Parkinson’s Disease

Levodopa medication is the most efficient treatment for motor symptoms of Parkinson’s disease (PD). Levodopa significantly alleviates rigidity, rest tremor, and bradykinesia in PD. The severity of motor symptoms can be graded with UPDRS-III scale. Levodopa challenge test is routinely used to assess patients’ eligibility to deep-brain stimulation (DBS) in PD. Feasible and objective measurements to assess motor symptoms of PD during levodopa challenge test would be helpful in unifying the treatment. Twelve patients with advanced PD who were candidates for DBS treatment were recruited to the study. Measurements were done in four phases before and after levodopa challenge test. Rest tremor and rigidity were evaluated using UPDRS-III score. Electromyographic (EMG) signals from biceps brachii and kinematic signals from forearm were recorded with wireless measurement setup. The patients performed two different tasks: arm isometric tension and arm passive flexion–extension. The electromyographic and the kinematic signals were analyzed with parametric, principal component, and spectrum-based approaches. The principal component approach for isometric tension EMG signals showed significant decline in characteristics related to PD during levodopa challenge test. The spectral approach on passive flexion–extension EMG signals showed a significant decrease on involuntary muscle activity during the levodopa challenge test. Both effects were stronger during the levodopa challenge test compared to that of patients’ personal medication. There were no significant changes in the parametric approach for EMG and kinematic signals during the measurement. The results show that a wireless and wearable measurement and analysis can be used to study the effect of levodopa medication in advanced Parkinson’s disease.

The Evolution of Financial Integration on Selected European Stock Markets: a Dynamic Principal Component Approach

The goal of this paper is to recognize the dynamics of financial integration across the European stock markets over the last two decades. We investigate two groups of markets: (1) three developed European markets in the U.K., France, and Germany; and (2) three emerging Central and Eastern European markets in Poland, the Czech Republic, and Hungary (CEE–3). The evolution of the integration process is analyzed using a dynamic principal component approach. The index of integration serves as a robust measure of integration. The empirical results reveal that the dynamics of integration across the whole group of markets increased significantly following the CEEC–3’s accession to the European Union. An inverted U‑shape in the index of integration has been found in this case. Moreover, the average index of integration was significantly different during the Global Financial Crisis compared to the pre‑crisis period.

Advances in Principal Balances for Compositional Data

Compositional data analysis requires selecting an orthonormal basis with which to work on coordinates. In most cases this selection is based on a data driven criterion. Principal component analysis provides bases that are, in general, functions of all the original parts, each with a different weight hindering their interpretation. For interpretative purposes, it would be better to have each basis component as a ratio or balance of the geometric means of two groups of parts, leaving irrelevant parts with a zero weight. This is the role of principal balances, defined as a sequence of orthonormal balances which successively maximize the explained variance in a data set. The new algorithm to compute principal balances requires an exhaustive search along all the possible sets of orthonormal balances. To reduce computational time, the sets of possible partitions for up to 15 parts are stored. Two other suboptimal, but feasible, algorithms are also introduced: (i) a new search for balances following a constrained principal component approach and (ii) the hierarchical cluster analysis of variables. The latter is a new approach based on the relation between the variation matrix and the Aitchison distance. The properties and performance of these three algorithms are illustrated using a typical data set of geochemical compositions and a simulation exercise.

A Principal Component Approach to Measuring Investor Sentiment in Hong Kong

In light of the increasing integration between China and Hong Kong, this paper develops a new market sentiment index for the Hong Kong stock market by including the CSI 300 index of the Chinese equity market. A threshold regression model using the sentiment index as a threshold variable is estimated to capture the state of the Hong Kong stock market.

Students’ Perceptions of Teaching and Learning Practices: A Principal Component Approach

Students’ attendance and engagement with teaching and learning practices is perceived as a critical element for academic performance. Even with stipulated attendance policies, students still choose not to engage. The study employed a principal component analysis to analyze first- and second-year students’ perceptions of the importance of the 12 teaching and learning practices used in the Economics modules. The results showed that first year students perceive lecturer consultation, ADO consultation, and revision classes as the most beneficial practices for their academic performance. Second-year students recognize interactive group learning practices as most beneficial for their academic performance; they also perceive weekly tutorials, PowerPoint lectures, small group tutorials, and revision classes as contributing the most to academic performance. Self-study and e-learning are perceived as the least beneficial by both streams of students. The main conclusion from this study was that first-year students are more likely to be solitary learners and prefer teaching and learning practices that involve one-on-one interaction with the instructor. On the other hand, second-year students tend to be more social learners, preferring teaching and learning practices that are in a group setup. This is a possible explanation of why they do not attend or engage with some teaching and learning practices.

A multifactor approach to forecasting Romanian gross domestic product (GDP) in the short run.

The purpose of this paper is to investigate the application of a generalized dynamic factor model (GDFM) based on dynamic principal components analysis to forecasting short-term economic growth in Romania. We have used a generalized principal components approach to estimate a dynamic model based on a dataset comprising 86 economic and non-economic variables that are linked to economic output. The model exploits the dynamic correlations between these variables and uses three common components that account for roughly 72% of the information contained in the original space. We show that it is possible to generate reliable forecasts of quarterly real gross domestic product (GDP) using just the common components while also assessing the contribution of the individual variables to the dynamics of real GDP. In order to assess the relative performance of the GDFM to standard models based on principal components analysis, we have also estimated two Stock-Watson (SW) models that were used to perform the same out-of-sample forecasts as the GDFM. The results indicate significantly better performance of the GDFM compared with the competing SW models, which empirically confirms our expectations that the GDFM produces more accurate forecasts when dealing with large datasets.

Diffusion Indexes With Sparse Loadings

The use of large-dimensional factor models in forecasting has received much attention in the literature with the consensus being that improvements on forecasts can be achieved when comparing with standard models. However, recent contributions in the literature have demonstrated that care needs to be taken when choosing which variables to include in the model. A number of different approaches to determining these variables have been put forward. These are, however, often based on ad hoc procedures or abandon the underlying theoretical factor model. In this article, we will take a different approach to the problem by using the least absolute shrinkage and selection operator (LASSO) as a variable selection method to choose between the possible variables and thus obtain sparse loadings from which factors or diffusion indexes can be formed. This allows us to build a more parsimonious factor model that is better suited for forecasting compared to the traditional principal components (PC) approach. We provide an asymptotic analysis of the estimator and illustrate its merits empirically in a forecasting experiment based on U.S. macroeconomic data. Overall we find that compared to PC we obtain improvements in forecasting accuracy and thus find it to be an important alternative to PC. Supplementary materials for this article are available online.

Environmental vulnerability evaluation using a spatial principal components approach in the Daxing’anling region, China

Identifying environmentally vulnerable regions is an important aspect of forest resource management, especially in boreal forest ecosystems that exhibit sensitivity to climate change. In this study, an environmental vulnerability index was constructed to describe the vulnerability status of the ecosystem in the Daxing’anling region, Heilongjiang Province, China. Thirteen variables related to exposure, sensitivity and adaptive capacity of the ecosystem were selected and integrated into a comprehensive index using spatial principal component analysis. The vulnerability within each part of the study area was then classified into one of five levels, including potential, slight, light, medium and heavy vulnerability, based on the numerical values. Results showed that the degree of vulnerability was unevenly distributed throughout the Daxing’anling region. The highest environmental vulnerability index value was approximately 0.86 in the southern and central areas, suggesting that these regions are the most vulnerable to environmental changes. The lowest value was approximately 0.036 in the eastern region, which indicates a relatively high-quality environment that is less vulnerable to environmental changes. The results of the environmental vulnerability evaluation may be helpful for decision makers by providing a comparatively rational decision making tool for planning and implementing effective forest resource management.

Optimizing future experiments of cosmic far-infrared background: a principal component approach

The anisotropies of cosmic far-infrared background (CFIRB) probe the star formation rate (SFR) of dusty star-forming galaxies as a function of dark matter halo mass and redshift. We explore how future CFIRB experiments can optimally improve the SFR constraints beyond the current measurements of Planck. We introduce a model-independent, piecewise parametrization for SFR as a function of halo mass and redshift, and we calculate the Fisher matrix and principal components of these parameters to estimate the SFR constraints of future experiments. We investigate how the SFR constraints depend on angular resolution, number and range of frequency bands, survey coverage, and instrumental sensitivity. We find that the angular resolution and the instrumental sensitivity play the key roles. Improving the angular resolution from 20 to 4 arcmin can improve the SFR constraints by 1.5 - 2.5 orders of magnitude. With the angular resolution of Planck, improving the sensitivity by 10 or 100 times can improve the SFR constraints by one or two orders of magnitude, and doubling the number of frequency bands can also improve the SFR constraints by an order of magnitude. We find that survey designs like the Cosmic Origins Explorer (CORE) are very close to the optimal design for improving the SFR constraints at all redshifts, while survey designs like LiteBIRD and CMB-S4 can significantly improve the SFR constraints at z>3.

Principal component and multivariate statistical approach for evaluation of hydrochemical characterization of fluoride-rich groundwater of Shaslar Vagu watershed, Nalgonda District, India

In order to assess the impact of fluoride-rich groundwater of Shasilair Vagu watershed on groundwater regime, more than hundred groundwater samples for pre- and post-monsoon seasons were collected from bore wells/dug wells and analyzed for major ions. Water quality analysis of major ion chemistry shows elevated concentration of fluoride in groundwater samples. The fluoride concentration ranges from 1.4 to 5.9 mg/l and 1.5 to 5.8 mg/l in pre- and post-monsoons, respectively. The result clearly shows that the seasonal variation of fluoride in groundwater is due to recharge of rain water during monsoon. The water quality data was analyzed by hydrochemical facies (Piper diagram), Gibbs plot, and various plots. Plots of Na versus Cl, Ca versus SO4, and (Na+Cl)-(SO4+HCO3) versus (Na+K-Cl) shows positive and negative values, indicating that their source of high concentration are aquifer, evapotranpiration, and other anthropogenic sources. Saturation index of halite and gypsum shows that all groundwater samples were undersaturated and suggests that carbonate minerals influence the concentration. Using multivariate statistical techniques, viz., principal component (factor analysis and cluster analysis), the analysis brought out impact of intensity of excess use of fertilizers and excess withdrawal of groundwater regime. Multivariate statistical techniques are potential tools and provide greater precision for identifying contaminant parameter linkages.

Relationships between GAT1 and PTSD, Depression, and Substance Use Disorder.

Post-traumatic stress disorder (PTSD), Major Depressive Disorder (MDD), and Substance Use Disorder (SUD) have large public health impacts. Therefore, researchers have attempted to identify those at greatest risk for these phenotypes. PTSD, MDD, and SUD are in part genetically influenced. Additionally, genes in the glutamate and gamma-aminobutyric acid (GABA) system are implicated in the encoding of emotional and fear memories, and thus may impact these phenotypes. The current study examined the associations of single nucleotide polymorphisms in GAT1 individually, and at the gene level, using a principal components (PC) approach, with PTSD, PTSD comorbid with MDD, and PTSD comorbid with SUD in 486 combat-exposed veterans. Findings indicate that several GAT1 SNPs, as well as one of the GAT1 PCs, was associated with PTSD, with and without MDD and SUD comorbidity. The present study findings provide initial insights into one pathway by which shared genetic risk influences PTSD-MDD and PTSD-SUD comorbidities, and thus identify a high-risk group (based on genotype) on whom prevention and intervention efforts should be focused.

How investor sentiments spillover from developed countries to developing countries?

AbstractThis paper applies a sentiment index to check the influence of regional developed countries like Japan and Germany on the sentiments in regional developing countries like Pakistan and Turkey, respectively. The index has been created using a principal component approach with modified proxies. The results indicate that there is a significant influence of developed markets on developing markets also the sentiment index created in this study is good indicator regarding the return pattern of the stock exchange. This study has followed the footsteps of previous studies for methodology and the time period which is used ranged from 29 December 2004 to 31 December 2014. The weekly data has been collected with Wednesday prices so to avoid the white noise, start-of-the week and end-of-the week effect.