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This paper aimed to contextualize the process of public hospital providing services, based on the measurement of the performance of Federal University Hospitals (HUFs) of Brazil, using the technique of multivariate statistics of principal component analysis.

This research presented a descriptive and quantitative character, as well as exploratory purpose and followed the inductive logic, being empirically structured in two stages, that is, the application of principal component analysis (PCA) in four healthcare performance dimensions; subsequently, the full reapplication of principal component analysis in the most highly correlated variables, in module, with the first three main components (PC1, PC2 and PC3).

From the principal component analysis, considering mainly component I, with twice the explanatory power of the second (PC2) and third components (PC3), it was possible to evidence the efficient or inefficient behavior of the HUFs evaluated through the production of medical residency, by specialty area. Finally, it was observed that the formation of two groups composed of seven and eight hospitals, that is, Groups II and IV shows that these groups reflect similarities with respect to the scores and importance of the variables for both hospitals’ groups.

Among the main limitations it was observed that there was incomplete data for some HUFs, which made it impossible to search for information to explain and better contextualize certain aspects. More specifically, a limited number of hospitals with complete information were dealt with for 60% of SIMEC/REHUF performance indicators.

The use of PCA multivariate technique was of great contribution to the contextualization of the performance and productivity of homogeneous and autonomous units represented by the hospitals. It was possible to generate a large quantity of information in order to contribute with assumptions to complement the decision-making processes in these organizations.

Development of public policies with emphasis on hospitals linked to teaching centers represented by university hospitals. This also involved the projection of improvements in the reach of the efficiency of the services of assistance to the public health, from the qualified formation of professionals, both to academy, as to clinical practice.

The originality of this paper for the scenarios of the Brazilian public health sector and academic area involved the application of a consolidated performance analysis technique, that is, PCA, obtaining a rich work in relation to the extensive exploitation of techniques to support decision-making processes. In addition, the sequence and the way in which the content, formed by object of study and techniques, has been organized, generates a particular scenario for the measurement of performance in hospital organizations.

Participants in Internet net news bulletin board discussions can argue with others whom they do not know. The nature of each message in a discussion can be characterized by the frequency with which keywords appear in the message. This incidence or frequency can be summarized as a principal component score. By deriving two characteristic indexes – auto‐correlation coefficients and power spectra – from the principal component scores, we have come up with a novel method. Applying this method to analyze three large net news threads, the index plots revealed two‐generation cyclic fluctuations. Comparing these plots with actual points of conflict obtained by reading the message contents, a fairly good correlation was obtained between the two and it was found that most of the conflicts were among participants with different cultural backgrounds.

The purpose of this paper is to better understand the spatial structure of the Lyon urban area focusing on real estate. For this, two aims are formulated. The first aim is to identify and geographically analyse latent structure underlying apartment variables and location. The second aim is to decrease a number of explanatory variables in a hedonic model of real estate prices applying latent constructs.

For the first aim of a parsimonious representation among measured variables, exploratory factor analysis is applied. For the second aim of data reduction, principal component analysis (PCA) is used. The exploited regression methodologies are global and geographically weighted ordinary least squares.

Four factors are extracted, of which two represent apartment attributes and other two – location attributes. Principal components provide better insight into location attributes dividing the service employment centres into two geographical groups. The inclusion of principal components in hedonic price equation instead of initial location variables decreases goodness of fit, but does not gradually change non‐location estimates and other parameters.

Differently from previous applications of factor analysis and PCA in the real estate domain, oblique rotation is applied, which allows the extracted factors or components to be correlated. The scores of factors and components are interpolated from points to raster maps creating a continuous geographical distribution. Hedonic models with and without principal components are compared in detail.

This paper is to develop a quality measure to evaluate the quality level of child care service in the regional level. By utilizing the biannual intensive child care statistical reports, ten variables are integrated and summarized as a quality measure for child care service in regional level by employing Principal Component Analysis (PCA). Conclusively, it is possible to get a comprehensive measure and the measure obtained from data between 2003 and 2008 illustrates the difference in child care service quality among regions over years. With the measure developed by this research, each region can also get very good insight into what kinds of factors of child care service should be paid more attention to in order to improve the quality of its child care service. Moreover, the measure obtained in this paper is proven reliable and robust in that it reflects the quality of child care service in each region and gives us statistically uniform quality scores with a different data set.

This paper seeks to present a novel computational intelligence technique to generate concise neural network models for distributed dynamic systems.

The approach used in this paper is based on artificial neural network architectures that incorporate linear and nonlinear principal component analysis, combined with generalized dimensional analysis.

Neural network principal component analysis coupled with generalized dimensional analysis reduces input variable space by about 90 percent in the modeling of oil reservoirs. Once trained, the computation time is negligible and orders of magnitude faster than any traditional discretisation schemes such as fine‐mesh finite difference.

Finding the minimum number of input independent variables needed to characterize a system helps in extracting general rules about its behavior, and allows for quick setting of design guidelines, and particularly when evaluating changes in the physical properties of systems.

The methodology can be used to simulate dynamical systems characterized by differential equations, in an interactive CAD and optimization providing faster on‐line solutions and speeding up design guidelines.

Principal components analysis (PCA) is one of the foremost multivariate methods utilized in marketing and business research for data reduction, latent variable modeling, multicollinearity resolution, etc. However, while its optimal properties make PCA solutions unique, interpreting the results of such analyses can be problematic. A plethora of rotation methods are available for such interpretive uses, but there is no theory as to which rotation method should be applied in any given social science problem. In addition, different rotational procedures typically render different interpretive results. The paper aims to introduce restricted PCA (RPCA), which attempts to optimally derive latent components whose coefficients are integer‐constrained (e.g.: {−1,0,1}, {0,1}, etc.).

The paper presents two algorithms for deriving efficient solutions for RPCA: an augmented branch and bound algorithm for sequential extraction, and a combinatorial optimization procedure for simultaneous extraction of these constrained components. The paper then contrasts the traditional PCA‐derived solution with those obtained from both proposed RPCA procedures with respect to a published data set of psychographic variables collected from potential buyers of the Dodge Viper sports car.

This constraint results in solutions which are easily interpretable with no need for rotation. In addition, the proposed procedure can enhance data reduction efforts since fewer raw variables define each derived component.

The paper provides two algorithms for estimating RPCA solutions from empirical data.

The purpose of this paper is to present an adaptive numerical algorithm for forward kinematics analysis of general Stewart platform.

Unlike the convention of developing a set of kinematic equations and then solving them, an alternative numerical algorithm is proposed in which the principal components of link lengths are used as a bridge to analyze the forward kinematics of a Stewart platform. The values of link lengths are firstly transformed to the values of principal components through principal component analysis. Then, the computation of the values of positional variables is transformed to a two‐dimensional nonlinear minimization problem by using the relationships between principal components and positional variables. A hybrid Nelder Mead‐particle swarm optimizer (NM‐PSO) algorithm and a modified NM algorithm are used to solve the two‐dimensional nonlinear minimization problem.

Simulation experiments have been conducted to validate the numerical algorithm and experimental results show that the numerical algorithm is valid and can achieve good accuracy and high efficiency.

This paper proposes an adaptive numerical algorithm for forward kinematics analysis of general Stewart platform.

In order to determine the range of medium temperature zone of road asphalt, it is hoped that the evolution of viscoelastic characteristics of road asphalt under medium temperature state can be deeply explored.

In this paper, the needle penetration test and temperature scanning test were designed for 90# and 70# bitumen as test materials, and the boundary of medium temperature zone of 90# and 70# bitumen was accurately determined by data analysis method. A mathematical model was established based on principal component analysis, and a comprehensive evaluation index was proposed to evaluate the evolution of temperature viscoelastic characteristics of road asphalt by means of standardization and rotational dimensionality reduction.

The test results show that the medium temperature zone of 90# asphalt is [−5 ± 1°C, 38 ± 1°C], and the medium temperature zone of 70# asphalt is [0 ± 1°C, 51 ± 1°C]. According to the viscoelastic response of road asphalt in the medium temperature zone, the medium temperature zone can be divided into three evolution stages: weak viscoelastic stage, viscoelastic equilibrium stage, strong viscoelastic weak stage. Analysis based on the intrinsic viscosity fillip target describing the various intrinsic viscoelastic index represents the viscoelastic properties of bitumen from different angles, and limitations inherent stick fillip for target put forward the integrated the inherent stick fillip mark information, as well as targeted and accurate evaluation of road asphalt temperature comprehensive evaluation indexes in the evolution of the viscoelastic properties of I_M-T. Finally, the temperature data of asphalt pavement in several representative regions of China are compared with the determined medium temperature region, and it is proved that the research on the evolution of viscoelastic characteristics of asphalt pavement under the medium temperature condition has important practical significance.

The boundary of medium temperature zone of 90# and 70# base asphalt was determined, and the viscoelastic characteristic evolution of road asphalt under medium temperature state was studied deeply. Aiming at the limitation of intrinsic viscoelastic index, a comprehensive evaluation index IM-T which not only integrates the information of intrinsic viscoelastic index but also can accurately evaluate the evolution of temperature viscoelastic characteristics in road asphalt is proposed.

The purpose of this paper is to provide an analysis of the relationship between expected risk premia on property stocks and some major macroeconomic risk factors as reflected in the general business and financial conditions

Employs a three‐step estimation strategy (principal component analysis, GARCH (1,1) and GMM) to model the macroeconomic risk variables (GDP growth, INDP growth, unexpected inflation, money supply, interest rate and exchange rate) and relate them to the first and second moments on property stock excess returns of four major markets, namely, Singapore, Hong Kong, Japan and the UK. Macroeconomic risk is measured by the conditional volatility of macroeconomic variables.

The expected risk premia and the conditional volatilities of the risk premia on property stocks are time‐varying and dynamically linked to the conditional volatilities of the macroeconomic risk factors. However there are some disparities in the significance, as well as direction of impact in the macroeconomic risk factors across the property stock markets. Consequently there are opportunities for risk diversification in international property stock markets.

Results help international investors and portfolio managers deepen their understanding of the risk‐return relationship, pricing of macroeconomic risk as well as diversification implications in major Asia‐Pacific and UK property stock markets. Additionally, policy makers may play a role in influencing the expected risk premia and volatility on property stock markets through the use of macroeconomic policy.

This chapter measures financial integration in 10 industries over 4 different periods. We use two robust measures of integration: (i) the Pukthuanthong and Roll (2009)’s multi-factor R-square and (ii) the Volosovych (2011)’s integration index. Both measures, based on PCA, indicate that the difference between the level of integration over the period 2009–2012 (“Post-Lehman” era) and the level of integration over the period 1994–1998 (“Post-Liberalizations” era) is relatively high. In addition, the level of financial integration across international equity markets decreased during the late 1990s. This suggests that de jure integration does not necessarily improve de facto integration. Overall, our findings give rise to a “diversification benefits-insurance benefits trade-off.”