Search

Item response modeling (IRM/IRT) has been known to marketing scholars for a number of years. However, with the exception of some notable and important applications in international (cross-cultural) marketing and consumer behavior, even a cursory reading of marketing journals reveals a general lack of interest in applying IRM, despite its ability to provide highly useful measurement-related information. To address and hopefully remedy the paucity of adoption, we offer an application-oriented discussion of the utility of IRM for marketing and related business research to enable researchers to utilize the strengths and realize the benefits of this methodology in their empirical work. After a short discussion of the history of IRM, we focus on its fundamentals within a modern statistical framework based on the generalized linear model and closely related non-linear factor analysis. We then engage major concepts of IRM, including item characteristic curve, local independence, and dimensionality, as well as parameter estimation and information functions. The popular one- and two-parameter logistic models are next discussed, as is the issue of model selection. Several polytomous item response models are subsequently dealt with, followed by a discussion of multidimensional IRM and data illustrations of item response models using widely available software. References to exemplar marketing applications are provided along the way, and a discussion of limitations of IRM concludes the article. Keywords Dimensionality * Factor analysis * Generalized linear model * Item characteristic curve * Item response modeling * Latent variable modeling * Local independence * Non-linear factor analysis, The utility of item response modeling in marketing research Marketing and business researchers are becoming increasingly interested in investigating theoretical concepts that are not directly observable, such as consumer satisfaction, [...]

Abstract Purpose--The Islamic banking sector has been growing rapidly, particularly in the Gulf region. The purpose of this paper is to examine major barriers to service quality in Islamic banks [...]

A study presents a business failure prediction model using the emerging technique of decision trees and tests it numerically. Results indicate that the proposed model predicts business failure better than other commonly used approaches such as discriminant analysis.

The article studies the suitability of smooth support vector machines (SSVM) for predicting the default risk of companies using variable selection and over sampling. Findings suggest that how different models could be used jointly to support the decision-making process of loan officers.

The fault diagnosis of Gas Insulated Switchgear (GIS) partial discharge (PD) is significant for mastering the essence of defects within the GIS accurately and guiding its maintenance. This paper designed four typical single defects and three mixed defects in GIS, then PD 3-dimensional patterns were constructed based on mass sample gathered by the very-high frequency and high speeds sampling systems. As to the problems in linear Fisher analysis used for diagnosing the failures of PD, this paper puts forward kernel statistical uncorrelated optimum discriminant vectors (KSUODV) algorithm to solve the problem of non-linear feature extraction in High-dimensional feature space based on kernel method and to eliminate statistical correlation between transformed sample features. The recognition result of the PD patterns of seven defects obtained in the lab proves that KSUODV algorithm for recognition is prior to SUODV algorithm. Index Terms--Gas insulated switchgear (GIS), partial discharge (PD), Pattern recognition, Kernel, Fisher discriminant analysis.

Many situations arise when it is desired to recover from a noisy mixture of data both an estimate of a propagating source signal as well as its angular position. Accordingly, the problem of source separation and tracking for time-varying systems of moving sources is studied. Using techniques borrowed from independent component analysis (ICA) and numerical linear algebra, a processing solution is formulated. The ULV decomposition (ULVD), a member of the complete orthogonal decompositions (CODs) family is used to normalize and decorrelate the data. Since the ULVD is known to stably update and downdate with time-varying data, this step is named ULVD adaptive whitening. The ICA-based EASI (equivariant adaptive separation based on independence) algorithm is adapted to provide not only separated sources but direction-of-arrival (DOA) estimates. Developed improvements to this technique are an adaptive filter method for estimating the mixing matrix from the newly estimated signals as well as a simple cross-over detection scheme. Finally, the developed solutions ate tested in simulation examples using meaningful performance measures.

Model selection of factor analysis (FA) model in radar high-resolution range profile (HRRP) statistical recognition is discussed. Results suggest an improvement in recognition performance with higher recognition efficiency.

The article describes the distinctively normalized joint diagonalization (DNJD) algorithm, a new approach to joint diagonalization for a set of symmetric matrices with a general matrix. The approach is simple, effective and computationally efficient in the context of blind source separation (BSS).

Abstract In the field of territorial economic growth, it is possible to identify important characteristics in the entrepreneurial structure, such as the structure of the size of firms, main activity [...]

This paper presents a unified criterion, Fisher + kernel criterion (FKC), for feature extraction and recognition. This new criterion is intended to extract the most discriminant features in different nonlinear spaces, and then, fuse these features under a unified measurement. Thus, FKC can simultaneously achieve nonlinear discriminant analysis and kernel selection. In addition, we present an efficient algorithm Fisher + kernel analysis (FKA), which utilizes the bilinear analysis, to optimize the new criterion. This FKA algorithm can alleviate the ill-posed problem existed in traditional kernel discriminant analysis (KDA), and usually, has no singularity problem. The effectiveness of our proposed algorithm is validated by a series of face-recognition experiments on several different databases. Index Terms--Bilinear analysis, discriminant analysis, face recognition, feature extraction, Fisher criterion, kernel selection.

Two fourth-order cumulant-based techniques for the estimation of the mixing matrix in underdetermined independent component analysis is studied. The number of sources that can be allowed is roughly quadratic in the number of observations.

Discriminant analysis is used to examine the impact of distance from a transit station on office rent and the value of land in Seoul.

A new feature extraction process is described for gait representation and recognition, which is based on the Radon transform of binary silhouettes. The new system on Gait Challenge database is used to show considerable improvements in recognition performance when compared to state-of-the-art methods for gait recognition.

A novel approach is presented to solve the supervised dimensionality reduction problem by encoding an image object as a general tensor of second or even higher order. The proposed multilinear discriminant analysis (MDA) algorithm based on higher order tensors is shown to have the potential to outperform the traditional vector-based subspace learning algorithms, especially in the cases with small sample sizes.

A novel recursive procedure is proposed for extracting discriminant features, termed recursive cluster-based linear discriminant (RCLD), which combines the ideas of recursive procedure, from recursive Fisher linear discriminant (RFLD), and cluster-based approach, from cluster-based linear discriminant (CLD). The performance of the proposed method is found to be better than other methods for all the face recognition problems.

High-dimensional data appear in many applications of data mining, machine learning, and bioinformatics. Feature reduction is commonly applied as a preprocessing step to overcome the curse of dimensionality. Uncorrelated Linear Discriminant Analysis (ULDA) was recently proposed for feature reduction. The extracted features via ULDA were shown to be statistically uncorrelated, which is desirable for many applications. In this paper, an algorithm called ULDA/QR is proposed to simplify the previous implementation of ULDA. Then, the ULDA/GSVD algorithm is proposed, based on a novel optimization criterion, to address the singularity problem which occurs in undersampled problems, where the data dimension is larger than the sample size. The criterion used is the regularized version of the one in ULDA/QR. Surprisingly, our theoretical result shows that the solution to ULDA/GSVD is independent of the value of the regularization parameter. Experimental results on various types of data sets are reported to show the effectiveness of the proposed algorithm and to compare it with other commonly used feature reduction algorithms. Index Terms--Feature reduction, uncorrelated linear discriminant analysis, QR-decomposition, generalized singular value decomposition.

We address the problem of pose-invariant face recognition based on a single model image. To cope with novel view face images, a model of the effect of pose changes on face appearance must be available. Face images at an arbitrary pose can be mapped to a reference pose by the model yielding view-invariant representation. Such a model typically relies on dense correspondences of different view face images, which are difficult to establish in practice. Errors in the correspondences seriously degrade the accuracy of any recognizer. Therefore, we assume only the minimal possible set of correspondences, given by the corresponding eye positions. We investigate a number of approaches to pose-invariant face recognition exploiting such a minimal set of facial features correspondences. Four different methods are proposed as pose-in-variant face recognition 'experts' and combined in a single framework of expert fusion. Each expert explicitly or implicitly realizes the three sequential functions jointly required to capture the nonlinear manifolds of face pose changes: representation, view transformation, and class discriminative feature extraction. Within this structure, the experts are designed for diversity. We compare a design in which the three stages are sequentially optimized with two methods which employ an overall single nonlinear function learnt from different view face images. We also propose an approach exploiting a three-dimensional face data. A lookup table storing facial feature correspondences between different pose images, found by 3-D face models, is constructed. The designed experts are different in their nature owing to different sources of information and architectures used. The proposed fusion architecture of the pose-in-variant face experts achieves an impressive accuracy gain by virtue of the individual experts diversity. It is experimentally shown that the individual experts outperform the classical linear discriminant analysis (LDA) method on the XM2VTS face data set consisting of about 300 face classes. Further impressive performance gains are obtained by combining the outputs of the experts using different fusion strategies. Index Terms--Expert fusion, face image synthesis, linear discriminant analysis (LDA), multiple classifier system, pose-in-variant face recognition.

A discriminant analysis is conducted to compare standard two stage discriminant models with linear programming and integer programming methods.

A study analyses eight discriminant analysis approaches to predict group membership of sampled observation.

A study, using a factor analytic approach examines the marginal choice between debt and equity securities, is presented.

ABSTRACT The quality of silk cocoons is determined by a number of characteristics. Each of these characteristics measures a different aspect of the quality. In the absence of a single [...]

A model of statistical discrimination with true interaction effects between groups is developed. The discrimination in terms of human capital exploitation reveals that incentives to acquire human capital are affected not only by investment behavior in the own group, but also by human capital investments in the other group.

Application of factor analysis, for identification of consumer preferences pertaining to protection of natural areas in Portugal, is discussed.

Consumption and demographic profiles are provided for various barter and vendor groups. Those individuals who operate in the informal economy as vendors and barterers are the youngest, have the highest educational levels and the highest income levels.

Linear discrimination analysis was compared with several neural network techniques to investigate their effectiveness in providing reliable and accurate bankruptcy predictions among companies in the oil and gas industry. Results showed that probabilistic neural networks without pattern normalization and Fisher discriminant analysis produced good estimation results. However, discriminant analysis produced the best results for bankrupt firms.

This paper presents a nonlinear supervised feature extraction algorithm that combines Fisher's criterion function with a preliminary perceptron-like nonlinear projection of vectors in pattern space. Its main motivation is to combine the approximation properties of multilayer perceptrons (MLP's) with the target free nature of Fisher's classical discriminant analysis. In fact, although MLP's provide good classifiers for many problems, there may be some situations, such as unequal class sizes with a high degree of pattern mixing among them, that may make difficult the construction of good MLP classifiers. In these instances, the features extracted by our procedure could be more effective. After the description of its construction and the analysis of its complexity, we will illustrate its use over a synthetic problem with the above characteristics. Index Terms - Fisher's discriminant analysis, mean error probability, multilayer perceptrons, nonlinear projections.

The textured nature of most natural land cover units as represented in remotely sensed imagery causes limited results of per-pixel classifications. The segmentation algorithm, Iterative Mutually Optimum Region Merging (IMORM), is presented and used to partition images into elements that are thereafter classified by Linear Canonical Discriminant Analysis and a Maximum Likelihood allocation rule. This per-segment approach results in much higher accuracy than the conventional per-pixel approach. Furthermore, separability matrices indicate that many !and cover categories cannot be correctly defined by per-pixel statistics.

Deriving classification rules or decision trees from examples is an important problem. When there are too many features, discarding weak features before the derivation process is highly desirable. When there are numeric features, they need to be discretized for the rule generation. We present a new approach to these problems. Traditional techniques make use of feature merits based on either the information theoretic, or the statistical correlation between each feature and the class. We instead assign merits to features by finding each feature's 'obligation' to the class discrimination in the context of other features. The merits are then used to rank the features, select a feature subset, and discretize the numeric variables. Experience with benchmark example sets demonstrates that the new approach is a powerful alternative to the traditional methods. This paper concludes by posing some new technical issues that arise from this approach. Index Terms - Feature analysis, classification modeling, discretization, feature merit, feature selection.

A 'Nil model' Monte Carlo simulation study was conducted to evaluate the factors that influence resubstitution accuracy. Five factors, namely, sample size, number of candidate features, number of selected features, the proportion of sample sizes among the outcome groups and the correlation of the features were investigated. It was found that resubstitution bias is reduced if sample size is increased, the number of candidate features is decreased, the number of selected features is decreased, and the proportion of samples from each outcome groups is equalized.

The concept, structures, and algorithms of principal feature classification (PFC) are presented in this paper. PFC is intended to solve complex classification problems with large data sets. A PFC network is designed by sequentially finding principal features and removing training data which has already been correctly classified. PFC combines advantages of statistical pattern recognition, decision trees, and artificial neural networks (ANN's) and provides fast learning with good performance and a simple network structure. For the real-world applications of this paper, PFC provides better performance than conventional statistical pattern recognition, avoids the long training times of backpropagation and other gradient-decent algorithms for ANN's, and provides a low-complexity structure for realization. Index Terms - Statistical pattern recognition, neural networks, decision trees, discriminant analysis, classification, signal processing.

Journal of the Market Research Society, Volume 10, Number 3, July 1968 The model Like many other analysis techniques, factor analysis is appropriate to data which conform to a certain [...]

A comprehensive review of literature on business failures was conducted to determine methods of forecasting business failure. A total of 158 articles were compiled and classified according to country, industrial sector, time period, financial ratios and models or methods used. Findings revealed that the discriminant analysis (DA) procedure was the most popular method for business failure investigations. However, many procedures were developed in the 1980s to overcome the restrictions of DA. The most significance financial ratios were found in the solvency category.

Prolonged exposure to weightlessness is known to produce a variety of cardiovascular changes, some of which may influence the astronaut's performance during a mission. In order to find a reliable indicator of cardiovascular adaptation to weightlessness, we analyzed data from nine male subjects after a 24-hour period of normal activity and after a period of simulated weightlessness produced by two hours in a launch position followed by 20 hours of 6 [degrees] head-down tilt plus pharmacologically induced diuresis (furosemide). Heart rate, arterial pressure, thoracic fluid index, and radial flow were analyzed. Autoregressive spectral estimation and decomposition were used to obtain the spectral components of each variable from the subjects in the supine position during pre- and post-simulated weightlessness. We found a significant decrease in heart rate power and an increase in thoracic fluid index power in the high frequency region (0.2-0.45 Hz) and significant increases in radial flow and arterial pressure powers in the low frequency region (

A discriminant model identifies the high-risk program modules of a software by relating the functional enhancement (FE) activity to the distribution of software defects. Accounting for the defect impact provides improvement in discriminant results. Examination of the misclassification functions used in the model suggest that the level of routines in the calling hierarchy introduces variation in defect distribution, and that the impact of a defect relates to the risk it poses.

Substantial efforts have been made to improve R&D project termination decisions. One major stream of research has demonstrated that discriminant functions can distinguish between successful and nonsuccessful projects. It is of considerable importance to test the validity of discriminant functions. In the present study we show the applicability of a discriminant function that was originally developed by Balachandra for US R&D projects to data from British and German R&D projects. The results are mixed. It is encouraging that a very high percentage of projects can be correctly classified as successes or failures. But the discriminant functions vary between countries. Thus, the results reveal different management perceptions of project success factors in these countries.

Psychographic segmentation is a marketing tool used by companies to improve their marketing strategies and enhance their competitiveness in the market. Discriminant analysis is a statistical procedure that enables firms to identify the risk factors associated by analyzing the data collected during cluster analysis. It aims to determine the significance of the profiles in the clusters and identify which variable causes the differences in the profile of the risk clusters.