Channel estimation of dual-hop MIMO relay system via parallel factor analysis

The optimal source precoding matrix and relay amplifying matrix have been developed in recent works on multiple-input multiple-output (MIMO) relay communication systems assuming that the instantaneous channel state information (CSI) is available. However, in practical relay communication systems, the instantaneous CSI is unknown, and therefore, has to be estimated at the destination node. In this paper, we develop a novel channel estimation algorithm for two-hop MIMO relay systems using the parallel factor (PARAFAC) analysis. The proposed algorithm provides the destination node with full knowledge of all channel matrices involved in the communication. Compared with existing approaches, the proposed algorithm requires less number of training data blocks, yields smaller channel estimation error, and is applicable for both one-way and two-way MIMO relay systems with single or multiple relay nodes. Numerical examples demonstrate the effectiveness of the PARAFAC-based channel estimation algorithm.

Underdetermined blind source separation by parallel factor analysis in time-frequency domain

This paper presents a new time-frequency approach to the underdetermined blind source separation using the parallel factor decomposition of third-order tensors. Without any constraint on the number of active sources at an auto-term time-frequency point, this approach can directly separate the sources as long as the uniqueness condition of parallel factor decomposition is satisfied. Compared with the existing two-stage methods where the mixing matrix should be estimated at first and then used to recover the sources, our approach yields better source separation performance in the presence of noise. Moreover, the mixing matrix can be estimated at the same time of the source separation process. Numerical simulations are presented to show the superior performance of the proposed approach to some of the existing two-stage blind source separation methods that use the time-frequency representation as well.

Factor analysis of partners' commitment to risk management in public-private partnership projects

Purpose – The purpose of this paper is to investigate and uncover key determinants that could explain partners' commitment to risk management in public-private partnership projects so that partners' risk management commitment is taken into the consideration of optimal risk allocation strategies.

Design/methodology/approach – Based on an extensive literature review and an examination of the purchasing power parity (PPP) market, an industry-wide questionnaire survey was conducted to collect the data for a confirmatory factor analysis. Necessary statistical tests are conducted to ensure the validity of the analysis results.

Findings – The factor analysis results show that the procedure of confirmatory factor analysis is statistically appropriate and satisfactory. As a result, partners' organizational commitment to risk management in public-private partnerships can now be determined by a set of components, namely general attitude to a risk, perceived one's own ability to manage a risk, and the perceived reward for bearing a risk.

Practical implications – It is recommended, based on the empirical results shown in this paper, that, in addition to partners' risk management capability, decision-makers, both from public and private sectors, should also seriously consider partners' risk management commitment. Both factors influence the formation of optimal risk allocation strategies, either by their individual or interacting effects. Future research may therefore explore how to form optimal risk allocation strategies by integrating organizational capability and commitment, the determinants and measurement of which have been established in this study.

Originality/value – This paper makes an original contribution to the general body of knowledge on risk allocation in large-scale infrastructure projects in Australia adopting the procurement method of public-private partnership. In particular, this paper has innovatively established a measurement model of organisational commitment to risk management, which is crucial to determining optimal risk allocation strategies and in turn achieving project success. The score coefficients of all obtained components can be used to construct components by linear combination so that commitment to risk management can be measured. Previous research has barely focused on this topic.

Confirmatory factor analysis of the Beck Depression inventory in obese individuals seeking surgery

Background : The Beck Depression Inventory (BDI) is frequently employed as measure of depression in studies of obesity. The aim of the study was to assess the factorial structure of the BDI in obese patients prior to bariatric surgery.

Methods : Confirmatory factor analysis was conducted on the current published factor analyses of the BDI. Three published models were initially analysed with two additional modified models subsequently included. A sample of 285 patients presenting for Lap-Band® surgery was used.

Results : The published bariatric model by Munoz et al. was not an adequate fit to the data. The general model by Shafer et al. was a good fit to the data but had substantial limitations. The weight loss item did not significantly load on any factor in either model. A modified Shafer model and a proposed model were tested, and both were found to be a good fit to the data with minimal differences between the two. A proposed model, in which two items, weight loss and appetite, were omitted, was suggested to be the better model with good reliability.

Conclusions : The previously published factor analysis in bariatric candidates by Munoz et al. was a poor fit to the data, and use of this factor structure should be seriously reconsidered within the obese population. The hypothesised model was the best fit to the data. The findings of the study suggest that the existing published models are not adequate for investigating depression in obese patients seeking surgery.

Modelling multilevel data in multimedia : A hierarchical factor analysis approach

Multimedia content understanding research requires rigorous approach to deal with the complexity of the data. At the crux of this problem is the method to deal with multilevel data whose structure exists at multiple scales and across data sources. A common example is modeling tags jointly with images to improve retrieval, classification and tag recommendation. Associated contextual observation, such as metadata, is rich that can be exploited for content analysis. A major challenge is the need for a principal approach to systematically incorporate associated media with the primary data source of interest. Taking a factor modeling approach, we propose a framework that can discover low-dimensional structures for a primary data source together with other associated information. We cast this task as a subspace learning problem under the framework of Bayesian nonparametrics and thus the subspace dimensionality and the number of clusters are automatically learnt from data instead of setting these parameters a priori. Using Beta processes as the building block, we construct random measures in a hierarchical structure to generate multiple data sources and capture their shared statistical at the same time. The model parameters are inferred efficiently using a novel combination of Gibbs and slice sampling. We demonstrate the applicability of the proposed model in three applications: image retrieval, automatic tag recommendation and image classification. Experiments using two real-world datasets show that our approach outperforms various state-of-the-art related methods.

On exploratory factor analysis: a review of recent evidence, an assessment of current practice, and recommendations for future use

Exploratory factor analysis (hereafter, factor analysis) is a complex statistical method that is integral to many fields of research. Using factor analysis requires researchers to make several decisions, each of which affects the solutions generated. In this paper, we focus on five major decisions that are made in conducting factor analysis: (i) establishing how large the sample needs to be, (ii) choosing between factor analysis and principal components analysis, (iii) determining the number of factors to retain, (iv) selecting a method of data extraction, and (v) deciding upon the methods of factor rotation. The purpose of this paper is threefold: (i) to review the literature with respect to these five decisions, (ii) to assess current practices in nursing research, and (iii) to offer recommendations for future use. The literature reviews illustrate that factor analysis remains a dynamic field of study, with recent research having practical implications for those who use this statistical method. The assessment was conducted on 54 factor analysis (and principal components analysis) solutions presented in the results sections of 28 papers published in the 2012 volumes of the 10 highest ranked nursing journals, based on their 5-year impact factors. The main findings from the assessment were that researchers commonly used (a) participants-to-items ratios for determining sample sizes (used for 43% of solutions), (b) principal components analysis (61%) rather than factor analysis (39%), (c) the eigenvalues greater than one rule and screen tests to decide upon the numbers of factors/components to retain (61% and 46%, respectively), (d) principal components analysis and unweighted least squares as methods of data extraction (61% and 19%, respectively), and (e) the Varimax method of rotation (44%). In general, well-established, but out-dated, heuristics and practices informed decision making with respect to the performance of factor analysis in nursing studies. Based on the findings from factor analysis research, it seems likely that the use of such methods may have had a material, adverse effect on the solutions generated. We offer recommendations for future practice with respect to each of the five decisions discussed in this paper.

Meaning in life in earlier and later older-adulthood : confirmatory factor analysis and correlates of the meaning in life questionnaire

OBJECTIVE: To validate the Meaning in Life Questionnaire (MLQ) in earlier and later older-adulthood, and examine its correlates. METHOD: Participants in earlier (n = 341, M age = 68.5) and later older-adulthood (n = 341, M age = 78.6) completed the MLQ and other measures. Confirmatory multigroup analysis, correlations, and regression models were conducted. RESULTS: A two-factor (presence and search), eight-item model of the MLQ had a good fit and was age-invariant. Presence and search for meaning were largely unrelated. Meaning was associated with life satisfaction, well-being across a range of domains, and psychological resources. Searching for meaning correlated negatively with these variables, but to a lesser degree in later older-adulthood. DISCUSSION: The MLQ is valid in older-adulthood. Meaning in life is psychologically adaptive in older-adulthood. Searching for meaning appears less important, especially in later older-adulthood. Findings are discussed in the context of aging and psychosocial development.

Operationalising the balanced scorecard : an initial analysis of factor structure

To date there has been very little empirical analysis of the Balanced Scorecard (BSC) within the marketing literature. With measuring performance being a central issue in marketing and the BSC being one of the most utilised approaches, this paper investigates the BSC and its factor structure. This research tested independently the “goodness-of-fit” of both the traditional four-factor model and a later five-factor model, which included an “employee/human resource” dimension. Data were collected from a sample of medium-tolarge Australian businesses. Factor analysis was conducted on the two alternative factor structures, revealing that the five-factor model fits the observed data as well as does the fourfactor model, supporting the inclusion of an “employee/human resource” perspective in future BSC models.

The development of the comprehensive child maltreatment scale

In this paper, data are presented from four studies that describe and evaluate the psychometric properties of the Comprehensive Child Maltreatment Scale (CCMS). This is a new measure that assesses five separate types of maltreatment experienced during childhood (sexual abuse, physical abuse, psychological maltreatment, neglect and witnessing family violence) and the existence of multi-type maltreatment. This scale is the only paper-and-pencil research scale available that assesses all five types of child maltreatment separately. In Studies 1 and 2, the CCMS for Adults was used to assess retrospective reports of adults' own childhood experiences (N=313). The parallel version of the CCMS for Parents was used in Studies 3 and 4 to assess parent reports of the experiences of children from 5 to 12 years of age (N=100). Adequate test-retest reliability and internal consistency were found for each of the scales of the CCMS for Adults and the CCMS for Parents. As well as performing an exploratory factor analysis, a criterion validity check on the CCMS for Adults revealed high correlations with appropriate subscales from the Child Abuse Trauma Scale. These preliminary data on the CCMS for Adults and Parents show that they are psychometrically sound and useful research tools in the study of multiple forms of child abuse and neglect. The CCMS for Adults and the CCMS for Parents allow for a simple yet comprehensive assessment of multi-type maltreatment.

Location dependency and sport sponsors : a factor analytic study

Theories explaining the mechanics of sport sponsorship relationships arc underdeveloped (Gilbert, 1 988; Hock, Gendall, & West, 1990; Pope, 1 998), bolstered by studies lacking systematic methods (Kuzma, Shanklin, & McCall, 1993) and tending toward broad, descriptive, macro-level analysis (Sandler & Shani, 1993). This paper attempts to redress this empirical chasm in a small way by examining an element of the sponsorship relationship. Specifically this paper explores the importance of one particular mode of sponsorship delivery: the location of a venue containing sponsor affiliations or what has been named location dependency. Location dependency of sport sponsors has been shown to be a pivotal determinant when devising sponsorship proposals or when assessing the attractiveness of a sponsorship opportunity (Wester- beek, 2000). Factor analysis was used to determine if sponsors' response patterns would deliver a number of constructs that could be related to the concept of location dependency. Factor analysis revealed five factors that principally reinforced the notion of location dependency of sponsorship. T-tests delivered significant differences between location dependent and location independent sponsors on some of the factors. The results of this study suggest that appreciating the concept of location dependency may assist companies in the effective discharge of their sponsorship decisions.

Testing the factor structure of the behavioral-intentions battery: an empirical study of the Australian banking industry

The five-factor ‘Behavioural-Intentions Battery’ was developed by Zeithaml, Berry and Parasuraman (1996), to measure customer behavioural and attitudinal intentions. The structure of this model was re-examined by Bloomer, de Ruyter and Wetzels (1999) across different service industries. They concluded that service loyalty is a multi dimensional construct consisting of four, not five, distinct dimensions. To date, neither model has been tested within a banking environment. This research independently tested the ‘goodness of fit’ of both the four and five-factor models, to data collected from branch bank customers. Data were collected via questionnaire with a sample of 348 banking customers. A confirmatory factor analysis was conducted upon the two opposing factor structures, revealing that the five-factor structure has a superior model fit; however, the fit is ‘marginal’.