Multiple-factor Model Research Articles

Fungicide treatments in winter wheat: The probability of profitability

The marginal return of fungicide applications against leaf blotch diseases in winter wheat fields were analysed based on data from field trials performed in 1996–2011 in Sweden. Yield increases from fungicide treatments were compared with data on growing conditions (precipitation, previous crop, nitrogen fertilization, soil type, and disease severity) using logistic regression. After identification and quantification of single factors with good predictive ability, multiple factor models were analysed. A model with five factors; rain days in April/May and three weeks before ear emergence, disease severity at ear emergence, soil type and previous crop, identified situations when a fungicide treatment gave a positive marginal return. The sensitivity and specificity of the model was evaluated by using receiver operating characteristic (ROC) curves.

Retracted: Gross product growth: a multiplicative factor model

The goal of this study is to propose a differential equation which relates a scalar economic indicator to the rate of change in renewable production factors. Based on this On the basis of this equation, I endeavour to build a multiplicative factor model of gross product growth and suggest an indicator that determines stagnation level and economic advance. Below the behavior of gross product change is addressed, by means of the vector analysis.Our analysis proves that the rate of gross product change equals the dynamic divergence integral of the product of an economy's peak output and the rate of change throughout all production factors' volumes. It is found that the rate of production factors volumes' change is equal to the dynamic divergence integral of the rate of factor change throughout the whole volume of factors. On the basis of the derived integral-differential equation, the sought indicator is obtained. Adequacy of the developed indicator is demonstrated through analysis of several European economies. The resulting model is a differential free-boundary problem. Its solution allows to make predictions about the gross product growth. The indicator, proposed in the study, constitutes a new tool for analysis of economic growth.

A population based twin study of DSM-5 maladaptive personality domains.

Personality disorders (PDs) can be partly captured by dimensional traits, a viewpoint reflected in the most recent Diagnostic and Statistical Manual of Mental Disorders-Fifth Edition (DSM-5) Alternative (Section III) Model for PD classification. The current study adds to the literature on the Alternative Model by examining the magnitude of genetic and environmental influences on 6 domains of maladaptive personality: negative emotionality, detachment, antagonism, disinhibition, compulsivity, and psychoticism. In a large, population-based sample (N = 2,293) of Norwegian male and female twin pairs, we investigated (a) if the domains demonstrated measurement invariance across gender at the phenotypic level, meaning that the relationships between the items and the latent factor were equivalent in men and women; and (b) if genetic and environmental influences on variation in these domains were equivalent across gender. Multiple group confirmatory factor modeling provided evidence that all 6 domain scale measurement models were gender-invariant. The best fitting biometric model for 4 of the 6 domains (negative emotionality, detachment, disinhibition, and compulsivity) was one in which genetic and environmental influences could be set invariant across gender. Evidence for sex differences in psychoticism was mixed, but the only clear evidence for quantitative sex differences was for the antagonism scale, with greater genetic influences found for men than women. Genetic influences across domains were moderate overall (19-37%), in line with previous research using symptom-based measures of PDs. This study adds to the very limited knowledge currently existing on the etiology of maladaptive personality traits. (PsycINFO Database Record

Factor Structure and Validation of the Controlling Behaviors Scale–Revised and Revised Conflict Tactics Scale

Recently, more attention is being paid to controlling behaviors within a continuum of intimate partner violence and abuse. However, it is unclear whether current scales are sufficiently valid to measure such behaviors. The current study assessed the factor structure and reliability of the revised Controlling Behaviors Scale (CBS-R) and the revised Conflict Tactics Scale (CTS2). Data were gathered from a U.K. general population sample ( N = 405). Confirmatory factor analyses were carried out on the CTS2 and the CBS-R, for both perpetration and victimization items, using the weighted least squares estimation with mean adjustment method. Multiple factor models were confirmed in the analysis of the CBS-R and CTS2 for perpetration and victimization items. Reliabilities for the factors were satisfactory across both scales. This is the first validation of the factor structure of the CBS-R and the findings suggests that this a valid and reliable scale for measuring controlling behaviors.

Contributions of condition measurements on the latent pavement condition by confirmatory factor analysis

ABSTRACTConfirmatory factor analysis (CFA) was utilized to analyze the contributions of condition measurements on the latent comprehensive pavement condition and the relative importance of the condition measurements. Both single-factor and multiple-factor model were built. An exploratory factor analysis (EFA) was conducted to determine the latent factors of the multiple CFA model. The results indicated that the multiple-factor model provided better model fit goodness than the single-factor model. All the observed variables including IRI and other nine distress variables were statistically significant for the comprehensive pavement condition. The strong indicators such as IRI, longitudinal cracking in wheel path, non-wheel path longitudinal cracking, fatigue cracking had greater weightings on the pavement condition indices than the weak ones’. Three latent factors were suggested by the multiple-factor CFA model, representing ride quality, early age cracking and severe pavement deterioration, respectively. Comparison between state routes and Interstates indicated that the more severe distresses such as fatigue and transverse cracking have higher contributions to the latent pavement condition of state routes, while less severe distresses such as raveling and patch have higher contributions to Interstates. This is because Interstates are maintained at the higher condition level and the less severe or early age distresses have higher impacts on the overall condition.

Значение культурно-исторической теории развития психики Л.С. Выготского для разработки современных моделей социального познания и методов психотерапии

The article acknowledges the situation of methodical crisis in modern research of social cognition related to the domination of reductive approaches that ignore the uniqueness of human psyche. Heuristicity of concepts of cultural-historical theory of psychological development of L.S. Vygotsky, which serves to overcome the apparent inconsistencies is substantiated. Models of social cognition based on the principles of cultural-historical psychology are described, those being the model of social cognition within phylogenesis of M. Tomasello, and the model of social cognition within ontogenesis of C. Fernyhough. Current situation in the area of mental health is reviewed from the standpoint of cultural-historical psychology, its specifics reflected in the increased burden on reflexive functions, that is, skills lying within the sphere of social cognition is substantiated. Modern psychotherapeutic apparatus directed to compensate social cognition deficits due to various psychiatric disorders is reviewed. The assumption that adolescense is sensitive period for the development of higher forms of social cognition is made, and a summary of researches supporting this assertion is presented. Main contradictions of modern-day maturing are enunciated. To conclude the presented theoretical analysis, a comprehensive multiple-factor model of social cognition is presented based on concepts of cultural-historical theory of L.S. Vygotsky.

Examining the reactive proactive questionnaire in adults in forensic and non‐forensic settings: A variable‐ and person‐based approach

The Reactive Proactive Questionnaire (RPQ) was originally developed to assess reactive and proactive aggressive behavior in children. Nevertheless, some studies have used the RPQ in adults. This study examines the reliability of the RPQ within an adult sample by investigating whether reactive and proactive aggression can be distinguished at a variable- and person-based level. Male adults from forensic samples (N = 237) and from the general population (N = 278) completed the RPQ questionnaire. Variable-based approaches, including factor analyses, were conducted to verify the two-factor model of the RPQ and to examine alternative factor solutions of the 23 items. Subsequently, a person-based approach, i.e., Latent Class Analysis (LCA), was executed to identify homogeneous classes of subjects with similar profiles of aggression in the observed data. The RPQ proved to have sufficient internal consistency. Multiple-factor models were examined, but the original two-factor model was statistically and theoretically considered as most solid and in line with previous research. The multi-level LCA identified three different classes of aggression severity (class 1 showed low aggressive behavior; class 2 subjects displayed modest aggression levels; and class 3 exhibited the highest level of aggressive behavior). In addition, class 1 and 2 showed more reactive than proactive aggression, whereas class 3 displayed comparable levels of reactive/proactive aggression. The RPQ appears to have clinical relevance for adult populations in the way that it can distinguish severity levels of aggression. Before the RPQ is implemented in adult populations, norm scores need to be developed. Aggr. Behav. 43:155-162, 2017. © 2016 Wiley Periodicals, Inc.

Reservoirs on the mountain rivers and their safety

Abstract Water resource issues and problems in the world's developing countries, present special challenges, as development of these countries significantly depends on the utilization of water resources. Georgia nestled between the Black Sea, Russia, and Turkey, and surrounded by the Caucasus Mountains, occupies a unique geographic space, which gives it strategic importance far beyond its size. Though blessed by its rich hydro resources, Georgia due to its uneven distribution, experiences some problems as the demand on water frequently doesn't coincide with water provision. As a result it causes acute deficit situation. Due to the global warming of the climate, it is expected that the fresh water amount will decrease in Georgia. This is why it is necessary to approach the use of water resources in a complex way by means of water reservoirs, which will enable attaining of a large economic effect. In the mountainous conditions filling of reservoirs take place in spring time, when snow and glaciers melt. In Georgia as in mountainous country, abundant rains take place, thus causing catastrophic flooding on rivers. In summer and winter water amount decreases 10 times and irrigation, water provision and energy production is impeded. Thus, the lack of water just like the excess amount of water causes damage. This is why it is needed to forecast water amount in water reservoirs for different periods of the year. But in a complex, mountainous terrain operative data of hydrometeorology is not sufficient for application of modern mathematical methods. We have elaborated multiple-factor statistical model for a forecast, which by means of different mathematical criteria and methods can simultaneously research the increase of the timeliness of forecasts and the level of their precision. We have obtained methodologies for short and long term forecasts of inflowing water properties in Georgia's main water reservoirs to further plan optimally and regulate water resources and their unimpeded and safe exploitation.

Policy failure or success? Detecting market failure in China's housing market

Due to a high vacancy rate of residential homes, housing prices remain sticky in most urban areas of China, which causes higher searching and bargaining costs. With an inefficient outcome, deadweight loss and market failure arises. To assess the Chinese government's housing policies in 2010, we develop a dynamic equilibrium model, in which we demonstrate how the sticky price results in market failure. We apply a multiple-factor panel data model to show that a high degree of market failure is associated with a high ratio of persistent components in the gap between price and equilibrium. As the persistent components will cause the market's instability, we can use the ratio between persistent and mean reverting components as an indicator to supervise the status of the housing market.We investigate the new and second-hand markets in 19 major cities, including 4 municipalities and 15 vice-provincial cities. Through our multiple-factor model, we explore the situation for each city. The results indicate these policies did improve the housing market's efficiency. It is therefore useful for the Chinese government to extend these policies to other areas to include not only big cities, but entire provinces which can improve its economic system efficiency and fairness even when its economic growth is slowing down.

Performance of classic multiple factor analysis and model fitting in crop modeling

Multivariate statistical analysis and regression, which are typical methods for crop modeling, have direct influence on the accuracy of model, but the applications of these methods usually depend on experiences. In this research, the performances of some common methods of statistical analysis and regression model were compared and verified, in order to avoid the blindness in crop modeling. The monitoring data of growth environment and photosynthesis of tomato, pumpkin and cucumber were obtained by PTM-48A. For the object variable of CO2 exchange rate, selectivity on the main environmental factors by correlation analysis and path analysis were quantitatively compared. The performances of four kinds of multivariate binomial regression equations were compared using a comprehensive aggregative indicator, and the effectiveness of modeling was verified with the selected optimized multivariate statistical analysis and regression equation. Results showed that path analysis was more comprehensive and effective than correlation to discrimination of the variables, especially the path analysis ruled out some suspected independent variables which were not really independent, and the pure quadratic was more suitable to crop modeling because of its simple structure and high accuracy when the data set was small. The conclusion of this research has a general applicability, and offers a useful reference and guide for the other study and application of crops’ modeling. Keywords: crop model, multivariate statistical analysis, path analysis, regression, comparison DOI: 10.3965/j.ijabe.20160902.1023 Citation: Jiang Z H, Zhang J, Yang C H, Rao Y, Li S W. Performance of classic multiple factor analysis and model fitting in crop modeling. Int J Agric & Biol Eng, 2016; 9(2): 119－126.

Human longevity is influenced by many genetic variants: evidence from 75,000 UK Biobank participants.

Variation in human lifespan is 20 to 30% heritable in twins but few genetic variants have been identified. We undertook a Genome Wide Association Study (GWAS) using age at death of parents of middle-aged UK Biobank participants of European decent (n=75,244 with father's and/or mother's data, excluding early deaths). Genetic risk scores for 19 phenotypes (n=777 proven variants) were also tested. In GWAS, a nicotine receptor locus (CHRNA3, previously associated with increased smoking and lung cancer) was associated with fathers' survival. Less common variants requiring further confirmation were also identified. Offspring of longer lived parents had more protective alleles for coronary artery disease, systolic blood pressure, body mass index, cholesterol and triglyceride levels, type-1 diabetes, inflammatory bowel disease and Alzheimer's disease. In candidate analyses, variants in the TOMM40/APOE locus were associated with longevity, but FOXO variants were not. Associations between extreme longevity (mother >=98 years, fathers >=95 years, n=1,339) and disease alleles were similar, with an additional association with HDL cholesterol (p=5.7×10-3). These results support a multiple protective factors model influencing lifespan and longevity (top 1% survival) in humans, with prominent roles for cardiovascular-related pathways. Several of these genetically influenced risks, including blood pressure and tobacco exposure, are potentially modifiable.

Testing for asset price bubbles: three new approaches

Testing for the existence of asset price bubbles is a difficult task. This paper shows that the local martingale theory of bubbles provides three new approaches that can be used to test for the existence of asset price bubbles, two of which are currently unexplored in the literature. The first is based on specifying a stochastic process for the asset's price process. The second exploits option price data, if options trade on the asset suspected to exhibit a price bubble. The third uses a multiple-factor model for the return process of the underlying asset.

Study on Prediction Model of Karstic Large Spring Water Level Dynamic Coupling Multiple Factors in Jinan

In karst regions, due to the heterogeneous features of karst medium, the characteristics of the groundwater flow turn to be of high complexity. Researchers have been seeking proper forecasting methods for karst water dynamic for many years. This paper, taking the spring in Jinan as an example, using regression analysis, analyzed the factors influencing spring water dynamic, and quantitatively evaluated the influencing coefficients of spring water level concerning rainfall, exploitation and recharge as well as the natural decay coefficient of spring water in dry seasons. The prediction model coupling multiple factors was built by investigating natural and anthropogenic factors influencing groundwater level, which could be used for forecasting dynamic of spring water in Jinan. The calculated value of model was highly coincided with the observed value. In consideration of the characteristics of uneven precipitation in Jinan, the suitable zones and volume of artificial recharge were investigated finally, which could help to sustain the spewing of Jinan springs significantly.

Normalisation theory: Does it accurately describe temporal changes in adolescent drunkenness and smoking?

The multiple risk model postulates that accumulating risk factors increase adolescent drunkenness and smoking. The normalisation theory adds to this by arguing that the relation between accumulative risk and drunkenness and smoking is dependent on the distribution of these behaviours in the larger population. More concretely, normalisation theory predicts that: (i) when population level use increases, low risk adolescents will be more likely to use alcohol and cigarettes; and (ii) adolescents facing multiple risk factors will be equally likely to use alcohol and cigarettes, regardless of trends in population level use. The current study empirically tests these assumptions on five waves of nationally representative samples of Israeli Jewish youth. Five cross-sectional waves of data from the Israeli Health Behaviour in School-aged Children survey for Jewish 10th graders were used. Logistic regression models measured the impact of changes in population level use across waves on drunkenness and smoking, and their association with differing levels of risk factors. Between zero and two risk factors, the risk of drunkenness and smoking increases for each additional risk factor. When reaching two risk factors, added risk does not significantly increase the likelihood of smoking and drunkenness. Changes in population level drunkenness and smoking did not systematically relate to changes in the individual level relationship between risk factors and smoking and drunkenness. The pattern of results in this study provides strong evidence for the multiple risk factor model and inconsistent evidence for the normalisation theory. [Sznitman SR, Zlotnick C, Harel-Fisch Y. Normalisation theory: Does it accurately describe temporal changes in adolescent drunkenness and smoking? Drug Alcohol Rev 2016;35:424-432].

Associations between five-factor model of the Positive and Negative Syndrome Scale and plasma levels of monoamine metabolite in patients with schizophrenia

The five-factor model of the Positive and Negative Syndrome Scale (PANSS) for schizophrenia symptoms is the most common multiple-factor model used in analyses; its use may improve evaluation of symptoms in schizophrenia patients. Plasma monoamine metabolite levels are possible indicators of clinical symptoms or response to antipsychotics in schizophrenia. We investigated the association between five-factor model components and plasma monoamine metabolites levels to explore the model's biological basis. Plasma levels of homovanillic acid (HVA), 3-methoxy-4-hydroxyphenylglycol (MHPG), and 5-hydroxyindoleacetic acid (5-HIAA) were measured using high-performance liquid chromatography in 65 Japanese patients with schizophrenia. Significant negative correlation between plasma 5-HIAA levels and the depression/anxiety component was found. Furthermore, significant positive correlation was found between plasma MHPG levels and the excitement component. Plasma HVA levels were not correlated with any five-factor model component. These results suggest that the five-factor model of the PANSS may have a biological basis, and may be useful for elucidating the psychopathology of schizophrenia. Assessment using the five-factor model may enable understanding of monoaminergic dysfunction, possibly allowing more appropriate medication selection. Further studies of a larger number of first-episode schizophrenia patients are needed to confirm and extend these results.

Research on selection of preventive maintenance training tasks for medical equipments

This paper studied the selection of training tasks of medical equipment preventive maintenance, to provide theoretical basis for standardized training design.Eight tasks of medical equipment PM process were quantitatively analyzed in multi factors based on the AHp and MFM, and the CDF decision trees model was compared with the selection standard.The studied identified the selection standards and criteria of the tasks, as well as reliable theoretical references for building the standardized training system. Key words: Medical equipment; Preventive maintenance; Multiple factor model; Analytic hierarchy process; Decision trees model; Standardized training

Advanced Idiosyncratic Risk and Multi-Factor Models

A common scenario risk analysis employs a multiple factor model with assumed changes in the factors to obtain changes in non-factor variables. This analysis is sometimes designated as a “predictive stress scenario”. We choose to designate the factor model as a multifactor “CAPM” model, also related to APT models. We are not concerned with the details of the factor model. The risks of the scenarios are evaluated for a portfolio of instruments depending on changes of the variables. A systemic problem with this factor model approach is that the empirical correlations between pairs of non-factor variables are not maintained. We designate “advanced idiosyncratic risk” or “AI-Risk” as the correction to the risk of a predictive stress CAPM model scenario that includes a better approximation to the physical correlations. We have developed a new, effective, and economical AI-Risk formalism. There are two parts to AI-Risk. The first part, which is actually standard, has independent normal random variables with no correlations; we designate that as part of the CAPM model to get the variances correctly reproduced; we believe this is standard practice. The second part contains the corrections to the correlations; this is new. We evaluate AI-Risk for some stock portfolios. We find that the AI-Risk can be important, typically between 10%-50% of the predictive stress risk. The amount depends on the ratio of long/short (“L/S”) position values, with larger AI-Risk for higher L/S ratios.A technical remark is that we use correlations as the metric for model improvement. Therefore we work with z-scores or “unit” returns with the empirical volatility divided out.Finally, we indicate the generalization to cross-sectional regression factor models.In summary this work achieves two results: 1. A parsimonious formalism incorporating correlated idiosyncratic risk that gives better agreement with given empirical correlations (so risk is calculated closer to the physical world). 2. Indications of substantial addition to risk due to correlated idiosyncratic contributions, with respect to the smaller risk obtained in the standard model with uncorrelated idiosyncratic risk.

Assessment of risk factors for patients with anatomical left ventricular aneurysm post acute ST-elevation myocardial infarction by use of multiple-risk-factor assessment models

To set up the multiple risk factors model of patients with anatomical left ventricular aneurysm (LVA) post acute ST-elevation myocardial infarction (STEMI) and quantitatively assess the pathopoiesis of all the factors. A total of 518 consecutive inpatients with acute STEMI hospitalized from June 2010 to December 2013 in our hospital were enrolled in this study, patients were divided into two groups: LVA group (n = 106, 20.5%) and non-LVA group (n = 412, 79.5%). All demographic and clinical data were collected by cardiologists. Finally, all of the risk factors for anatomical LVA in the acute STEMI patients were quantitatively analyzed by a binary logistic regression model. The multiple risk factors logistic regression model was set up for the anatomical LVA in patients with acute STEMI. Anterior wall myocardial infarction, occlusion of the left anterior descending branch, two or three vessels stenosis, high systolic blood pressure, sinus tachycardia and white blood cell count over 10 000 per microliter were all independent risk factors of the LVA in acute STEMI, with the odds ratio (OR) 18.21, 21.56, 4.22, 7.16, 1.98 and 1.57, respectively (all P < 0.05) . However, first medical contact less than 12 hours (OR = 0.60), collateral circulation of the coronary arteries(OR = 0.53), primary percutanous coronary intervention(OR = 0.23) and venous thrombolysis(OR = 0.12) were all protecting factors of the LVA in acute STEMI patients (all P < 0.05). Anterior wall STEMI, occlusion of the left anterior descending branch, two or three vessels stenosis, high systolic blood pressure, sinus tachycardia and white blood cell count over 10 000 per microlitre are independent risk factors of the LVA in acute STEMI patients. However, first medical contact less than twelve hours, collateral circulation of the coronary arteries, together with the primary percutanous coronary intervention and venous thrombolysis are protective factors of the LVA in patients with acute STEMI. It is important for cardiologists to assess the risks of LVA and make emergent and suitable efforts to reduce the risk of developing LVA in STEMI patients.

Modelling of OGTT curve identifies 1 h plasma glucose level as a strong predictor of incident type 2 diabetes: results from two prospective cohorts.

The relevance of the OGTT in predicting type 2 diabetes is unclear. We assessed the performance of 14 OGTT glucose traits in type 2 diabetes prediction. We studied 2,603 and 2,386 Europeans from the Botnia study and Malmö Prevention Project (MPP) cohorts with baseline OGTT data. Over a follow-up period of 4.94 years and 23.5 years, 155 (5.95%) and 467 (19.57%) participants, respectively, developed type 2 diabetes. The main outcome was incident type 2 diabetes. One-hour plasma glucose (1h-PG) was a fair/good predictor of incident type 2 diabetes in the Botnia study and MPP (AUC for receiver operating characteristic [AUCROC] 0.80 [0.77, 0.84] and 0.70 [0.68, 0.73]). 1h-PG alone outperformed the prediction model of multiple clinical risk factors (age, sex, BMI, family history of type 2 diabetes) in the Botnia study and MPP (AUCROC 0.75 [0.72, 0.79] and 0.67 [0.64, 0.70]). The same clinical risk factors added to 1h-PG modestly increased prediction for incident type 2 diabetes (Botnia, AUCROC 0.83 [0.80, 0.86]; MPP, AUCROC 0.74 [0.72, 0.77]). 1h-PG also outperformed HbA1c in predicting type 2 diabetes in the Botnia cohort. A 1h-PG value of 8.9 mmol/l and 8.4 mmol/l was the optimal cut-point for initial screening and selection of high-risk individuals in the Botnia study and MPP, respectively, and represented 30% and 37% of all participants in these cohorts. High-risk individuals had a substantially increased risk of incident type 2 diabetes (OR 8.0 [5.5, 11.6] and 3.8 [3.1, 4.7]) and captured 75% and 62% of all incident type 2 diabetes in the Botnia study and MPP. 1h-PG is a valuable prediction tool for identifying adults at risk for future type 2 diabetes.

A Generalized Multiple-Factor Asset Pricing Model

This paper derives a generalized multiple-factor asset pricing model using only the assumption of no arbitrage. This generalization differs from the standard multiple-factor pricing models in two ways. First, similar to standard models, a traded asset's expected return is linear in a finite number of traded risk-factor returns. Different from standard models, however, this model allows an infinite number of distinct risk-factors in the economy, where any asset's return depends on only a finite number of these. Different assets will, in general, depend on a different finite set of risk-factors. Second, positive alphas imply arbitrage opportunities and not just abnormal expected returns. This model can potentially explain many of the observed differences between existing multiple-factor asset pricing model implications and the empirical evidence.