Regularized Generalized Structured Component Analysis

Generalized structured component analysis (GSCA) has been proposed as a component-based approach to structural equation modeling. In practice, GSCA may suffer from multi-collinearity, i.e., high correlations among exogenous variables. GSCA has yet no remedy for this problem. Thus, a regularized extension of GSCA is proposed that integrates a ridge type of regularization into GSCA in a unified framework, thereby enabling to handle multi-collinearity problems effectively. An alternating regularized least squares algorithm is developed for parameter estimation. A Monte Carlo simulation study is conducted to investigate the performance of the proposed method as compared to its non-regularized counterpart. An application is also presented to demonstrate the empirical usefulness of the proposed method.     

Three-Mode Component Analysis with Crisp or Fuzzy Partition of Units

A new methodology is proposed for the simultaneous reduction of units, variables, and occasions of a three-mode data set. Units are partitioned into a reduced number of classes, while, simultaneously, components for variables and occasions accounting for the largest common information for the classification are identified. The model is a constrained three-mode factor analysis and it can be seen as a generalization of the REDKM model proposed by De Soete and Carroll for two-mode data. The least squares fitting problem is mathematically formalized as a constrained problem in continuous and discrete variables. An iterative alternating least squares algorithm is proposed to give an efficient solution to this minimization problem in the crisp and fuzzy classification context. The performances of the proposed methodology are investigated by a simulation study comparing our model with other competing methodologies. Different procedures for starting the proposed algorithm have also been tested. A discussion of some interesting differences in the results follows. Finally, an application to real data illustrates the ability of the proposed model to provide substantive insights into the data complexities.     

Regularized ELM bagging model for Tropical Cyclone Tracks prediction in South China Sea

This paper aims to improve the prediction accuracy of Tropical Cyclone Tracks (TCTs) over the South China Sea (SCS) with 24 h lead time. The model proposed in this paper is a regularized extreme learning machine (ELM) ensemble using bagging. The method which turns the original problem into quadratic programming (QP) problem is proposed in this paper to solve lasso and elastic net problem in ELM. The forecast error of TCTs data set is the distance between real position and forecast position. Compared with the stepwise regression method widely used in TCTs, 8.26 km accuracy improvement is obtained by our model based on the dataset with 70/1680 testing/training records. By contrast, the improvement using this model is 16.49 km based on a smaller dataset with 30/720 testing/training records. Results show that the regularized ELM bagging has a general better generalization capacity on TCTs data set.     

Bayesian multidimensional scaling for the estimation of a Minkowski exponent

The Minkowski property of psychological space has long been of interest to researchers. A common strategy has been calculating the stress in multidimensional scaling for many Minkowski exponent values and choosing the one that results in the lowest stress. However, this strategy has an arbitrariness problem—that is, a loss function. Although a recently proposed Bayesian approach could solve this problem, the method was intended for individual subject data. It is unknown whether this method is directly applicable to averaged or single data, which are common in psychology and behavioral science. Therefore, we first conducted a simulation study to evaluate the applicability of the method to the averaged data problem and found that it failed to recover the true Minkowski exponent. Therefore, a new method is proposed that is a simple extension of the existing Euclidean Bayesian multidimensional scaling to the Minkowski metric. Another simulation study revealed that the proposed method could successfully recover the true Minkowski exponent. BUGS codes used in this study are given in the Appendix.     

A convex approach for multicriteria decision making in hierarchical systems

In this paper hierarchical multicriteria optimization problems are addressed in a convex programming framework. It is assumed that the criteria are aggregated into a nonlinear function, which renders the problem nonseparable, in general. The projection of the problem onto the criteria space is used to obtain an equivalent separable problem, solved through a relaxation procedure implemented on basis of a multilevel structure. At the upper level of the structure, the decision making process involves the solution of a multicriteria problem formulated in the criteria space. The solution encountered at the upper level originates a lower level parametric optimization problem with separable structure that can be treated by standard coordination-decomposition techniques. The convergence of the overall procedure is ensured. The paper includes an application of the approach proposed for the control of dynamic systems with linear quadratic structure. © 1998 John Wiley & Sons, Ltd.     

Solution of the personnel classification problem with the method of optimal regions

The personnel classification problem is identified mathematically with other problems in the social and biological sciences. This mathematical problem is shown to be a special case of the general mathematical problem of linear programming. It is proposed here that the personnel classification problem may be solved directly by methods particularly appropriate to it as well as by the simplex method, which is a standard method for solving the general linear programming problem. The method of optimal regions is derived and illustrated in this paper.Much of the basic research covered in this paper was carried out while the author was working on the problem of personnel classification in his capacity as Consultant, Personnel Research Branch, Adjutant General's Office, Department of the Army. Some of the material was presented in a conference which was held at Personnel Research Branch in August, 1952. The author wishes to express his appreciation to the Department of the Army for permission to use these materials in this paper. The opinions expressed are those of the author and are not to be construed as official or as those of the Department of the Army.     

On portfolio management with value at risk and uncertain returns via an artificial neural network scheme

This paper focuses on the computation issue of portfolio optimization with scenario-based Value-at-Risk. The main idea is to replace the portfolio selection models with linear programming problems. According to the convex optimization theory and some concepts of ordinary differential equations, a neural network model for solving linear programming problems is presented. The equilibrium point of the proposed model is proved to be equivalent to the optimal solution of the original problem. It is also shown that the proposed neural network model is stable in the sense of Lyapunov and it is globally convergent to an exact optimal solution of the portfolio selection problem with uncertain returns. Several illustrative examples are provided to show the feasibility and the efficiency of the proposed method in this paper.     

A multi-objective mathematical programming problem with fuzzy relation constraints

In this study we discuss the problem of multi-objective mathematical programming with constraints defined by ‘max-min’ composite fuzzy relation equations. Since the feasible region is normally non-convex, the properties of the efficient points of a non-convex feasible region under multi-objectives are investigated and illustrated by examples. The necessary and sufficient conditions are proposed and proved. To facilitate decisions, a procedure that transforms these efficient points of an interval-valued decision space into a constant-valued decision space is proposed when the level of confidence is given by a decision maker. Then the transformed problem becomes a multi-attribute decision problem that can be evaluated by Yager's method to find the optimal alternative.     

An evaluation of exact methods for the multiple subset maximum cardinality selection problem

The maximum cardinality subset selection problem requires finding the largest possible subset from a set of objects, such that one or more conditions are satisfied. An important extension of this problem is to extract multiple subsets, where the addition of one more object to a larger subset would always be preferred to increases in the size of one or more smaller subsets. We refer to this as the multiple subset maximum cardinality selection problem (MSMCSP). A recently published branch‐and‐bound algorithm solves the MSMCSP as a partitioning problem. Unfortunately, the computational requirement associated with the algorithm is often enormous, thus rendering the method infeasible from a practical standpoint. In this paper, we present an alternative approach that successively solves a series of binary integer linear programs to obtain a globally optimal solution to the MSMCSP. Computational comparisons of the methods using published similarity data for 45 food items reveal that the proposed sequential method is computationally far more efficient than the branch‐and‐bound approach.     

Of science, meta-science, and clinical practice: the generalization of a generalization to a particular

Although science is the most powerful method for advancing knowledge and scientific and statistical formalisms are generally much sounder than impressionistic judgment, advanced methodological training (as it is presently comprised) is often of little direct use to practicing clinicians. Practitioners should be able to identify grossly deficient knowledge claims and clear scientific winners, but beyond this, the trick usually is to determine which scientific authority or authorities on whom to depend, a decision task that is ultimately to be tackled through the type of meta-scientific studies that Faust and Meehl have proposed. In applying scientific knowledge, the clinician usually must choose between the generalization of a scientific generalization to a particular versus the application of a clinical generalization to a particular. Determining the relative power of scientific generalizations developed outside, versus clinical generalizations developed within the setting of application, can be very difficult, and there are few established guides. A more advanced science of generalization, another meta-scientific problem, would allow sounder predictions about application to new domains and could greatly aid the practitioner.     

Of Science,Meta-Science,and Clinical Practice: The Generalization of a Generalization to a Particular

Although science is the most powerful method for advancing knowledge and scientific and statistical formalisms are generally much sounder than impressionistic judgment, advanced methodological training (as it is presently comprised) is often of little direct use to practicing clinicians. Practitioners should be able to identify grossly deficient knowledge claims and clear scientific winners, but beyond this, the trick usually is to determine which scientific authority or authorities on whom to depend, a decision task that is ultimately to be tackled through the type of meta-scientific studies that Faust and Meehl have proposed. In applying scientific knowledge, the clinician usually must choose between the generalization of a scientific generalization to a particular versus the application of a clinical generalization to a particular. Determining the relative power of scientific generalizations developed outside, versus clinical generalizations developed within the setting of application, can be very difficult, and there are few established guides. A more advanced science of generalization, another meta-scientific problem, would allow sounder predictions about application to new domains and could greatly aid the practitioner.     

Simultaneous Two-Way Clustering of Multiple Correspondence Analysis

A 2-way clustering approach to multiple correspondence analysis is proposed to account for cluster-level heterogeneity of both respondents and variable categories in multivariate categorical data. Specifically, in the proposed method, multiple correspondence analysis is combined with k-means in a unified framework in which k-means is applied twice to partition the object scores of respondents and the weights of variable categories. In this way, joint clusters that relate a subgroup of respondents exclusively to a subset of variable categories are obtained. The proposed method provides a low-dimensional map of displaying variable category points and the centroids of joint clusters simultaneously. In addition, it offers joint-cluster memberships of variable categories as well as respondents. A Monte Carlo study is conducted to assess the parameter recovery capability of the proposed method based on synthetic data. An empirical application concerning Korean consumers' preferences toward various underwear brands and attributes is presented to demonstrate the effectiveness of the proposed method as compared with 2 relevant extant approaches.     

Fuzzy Clusterwise Growth Curve Models via Generalized Estimating Equations: An Application to the Antisocial Behavior of Children

The growth curve model has been a useful tool for the analysis of repeated measures data. However, it is designed for an aggregate-sample analysis based on the assumption that the entire sample of respondents are from a single homogenous population. Thus, this method may not be suitable when heterogeneous subgroups exist in the population with qualitatively distinct patterns of trajectories. In this paper, the growth curve model is generalized to a fuzzy clustering framework, which explicitly accounts for such group-level heterogeneity in trajectories of change over time. Moreover, the proposed method estimates parameters based on generalized estimating equations thereby relaxing the assumption of correct specification of the population covariance structure among repeated responses. The performance of the proposed method in recovering parameters and the number of clusters is investigated based on two Monte Carlo analyses involving synthetic data. In addition, the empirical usefulness of the proposed method is illustrated by an application concerning the antisocial behavior of a sample of children.     

Real-time detection of the interaction between an upper-limb power-assist robot user and another person for perception-assist

Assisting aged population or a population with disabilities is a critical problem in today’s world. To compensate for their declined motor function, power-assist wearable robots have been proposed. In some cases, however, the cognitive function of these populations may have declined as well, and it may be insufficient to compensate only for their motor function deficiency. Perception-assist wearable robots, which can perceive environmental information using visual sensors attached to them, have been proposed to address this problem. This study addresses the problem of identifying motion intentions of the user of an upper-limb power-assist wearable robot, while the user engages in desired interactions with others. It is important to consider both the interacting parties in order to accurately predict proper interaction. Therefore, this paper presents an interaction recognition methodology by combining both the user’s motion intention and the other party’s motion intention with environmental information. A fuzzy reasoning model is proposed to semantically combine the motion intentions of both parties and environmental information. In this method, the motion intentions of both the user and the other party are simultaneously estimated using kinematic information and visual information, respectively, and they are employed for predicting the interactions between both parties. The effectiveness of the proposed approach is experimentally evaluated.     

An improvement of the Harman-Fukuda-method for the minres solution in factor analysis

The method proposed by Harman and Fukuda to treat the so-called Heywood case in the minres method in factor analysis i.e., the case where the resulting communalities are greater than one, involves the frequent solution of eigenvalue problems. A simple method to treat this problem requiring less computing time and enjoying higher numerical stability is described in this paper.     

An analytic solution of initial boundary value problem for 3D quasicrystals in half space

In this article, initial boundary value problem for 3D quasicrystals in half space is considered. An analytic method is proposed for special form of initial conditions and nonhomogeneous term. It is explained that a weak solution of the problem can be constructed in the similar form of data by using symbolic calculations.     

Validated intraclass correlation statistics to test item performance models

A new method, with an application program in Matlab code, is proposed for testing item performance models on empirical databases. This method uses data intraclass correlation statistics as expected correlations to which one compares simple functions of correlations between model predictions and observed item performance. The method rests on a data population model whose validity for the considered data is suitably tested and has been verified for three behavioural measure databases. Contrarily to usual model selection criteria, this method provides an effective way of testing under-fitting and over-fitting, answering the usually neglected question "does this model suitably account for these data?"     

Should physicians be Bayesian agents?

Because physicians use scientific inference for the generalizations of individual observations and the application of general knowledge to particular situations, the Bayesian probability solution to the problem of induction has been proposed and frequently utilized. Several problems with the Bayesian approach are introduced and discussed. These include: subjectivity, the favoring of a weak hypothesis, the problem of the false hypothesis, the old evidence/new theory problem and the observation that physicians are not currently Bayesians. To the complaint that the prior probability is subjective, Bayesians reply that there will be ultimate convergence, but the rebuttal to this is that there will not be uniform convergence. Secondly, since the Bayesian scheme favors a weak hypothesis, theories turn out to be a gratuitous risk. The problem with the false hypothesis comes out in the denominator of the theorem, revealing that a factor which is not a theory at all is being considered in the reasoning. On the old evidence/new theory problem old evidence cannot confirm a new theory so that the posterior probability will equal the prior probability. Finally, empiric studies have shown that current physicians are not Bayesians. But on consideration of Bayesian inference as a system of inference, it can be reasoned that physicians should be Bayesians. However, the problem of physicians' and patients' own subjectivity continue to plague this system of medical decision making.     

基于动态评估的取样法:经验取样法

研究结果的普适性是心理学研究的黄金标准之一.为达到此目标,研究者不断创造生态效度更高,也更具现实意义的研究方法.通过在现实情境下对被试多次重复测量进行数据收集的研究方法——经验取样法(experience sampling method),一经提出就备受研究者的青睐.文章在综合介绍经验取样法的概念、实施过程和数据处理的基础上,比较和阐述了该方法的优劣势,以及该方法的主要应用领域.文章最后对经验取样法的应用领域和未来发展作了展望.