Diagnosing item score patterns on a test using item response theory-based person-fit statistics.

Person-fit statistics have been proposed to investigate the fit of an item score pattern to an item response theory (IRT) model. The author investigated how these statistics can be used to detect different types of misfit. Intelligence test data were analyzed using person-fit statistics in the context of the G. Rasch (1960) model and R. J. Mokken's (1971, 1997) IRT models. The effect of the choice of an IRT model to detect misfitting item score patterns and the usefulness of person-fit statistics for diagnosis of misfit are discussed. Results showed that different types of person-fit statistics can be used to detect different kinds of person misfit. Parametric person-fit statistics had more power than nonparametric person-fit statistics. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Design development of a static sunshade using small scale modeling technique

The rising global demand for energy has triggered emphasis on conservation of energy. Buildings are one of the important energy consuming sectors. Passive solar architecture encompasses a wide range of strategies and options resulting in energy efficient building design and increased occupant's comfort. Passive solar design, aiming at increasing direct solar gains during winter period and to avoid overheating during summer period should make use of specific shading devices over energy efficient window. The static sunshades are most effective for solar control inside the buildings.Countries like India have composite climate, which can be classified under summer, winter and rainy season. Depending on the seasonal requirements, this paper introduces a new geometry of a static sunshade, designed by calculating the sun angles for the two dates. The static sun shading design methodology is validated with the help of small scale modeling experimentation technique, carried out in Pilani, Rajasthan (India). Although insulating materials can be used as a part of a building structure, its feasibility should be checked before particular application. In the present paper, the two small-scale experimental models of actual construction material with varying static sunshades, i.e. horizontal and the proposed one have been constructed and analyzed with the models of insulating material (Polyurethane Foam [PUF]). Depending upon the solar intersection over south facade wall, sunlit area and shaded area have been correlated with temperature inside the models to decide the effectiveness of the proposed sunshade.     

A complete VARMA modelling methodology based on scalar components

Abstract. This article proposes an extension to scalar component methodology for the identification and estimation of VARMA models. The complete methodology determines the exact positions of all free parameters in any VARMA model with a predetermined embedded scalar component structure. This leads to an exactly identified system of equations that is estimated using full information maximum likelihood.     

Green Tax Reforms and Computational Economics A Do-it-yourself Approach

The double or even triple dividend hypothesis of green tax reforms has beena major issue of dispute in both the scientific community and the politicalarena during the last decade. Theoretical analysis has provided a number ofimportant qualitative insights to the debate but lacks of actual policyrelevance due to very restrictive assumptions. Applied research that takes thestep from stylized analytical to complex numerical models usually comes as ablackbox to non-expert modelers. This paper aims at bridging the gap betweenstylized theoretical work and numerical analysis. We develop a flexible,interactive simulation model which is accessible underhttp://brw.zew.de. Users can specify their own green tax reforms andevaluate the induced economic and environmental effects. Based on illustrativesimulations, we demonstrate the usefulness of our do-it-yourself approach fora better understanding of the double (triple) dividend hypothesis.     

Data decomposition using independent component analysis with prior constraints

In many data analysis problems, it is useful to consider the data as generated from a set of unknown (latent) generators or sources. The observations we make of a system are then taken to be related to these sources through some unknown function. Furthermore, the (unknown) number of underlying latent sources may be less than the number of observations. Recent developments in independent component analysis (ICA) have shown that, in the case where the unknown function linking sources to observations is linear, such data decomposition may be achieved in a mathematically elegant manner. In this paper, we extend the general ICA paradigm to include a very flexible source model, prior constraints and conditioning on sets of intermediate variables so that ICA forms one part of a hierarchical system. We show that such an approach allows for efficient discovery of hidden representation in data and for unsupervised data partitioning.     

Can being scared cause tummy aches? Naive theories, ambiguous evidence, and preschoolers' causal inferences.

Causal learning requires integrating constraints provided by domain-specific theories with domain-general statistical learning. In order to investigate the interaction between these factors, the authors presented preschoolers with stories pitting their existing theories against statistical evidence. Each child heard 2 stories in which 2 candidate causes co-occurred with an effect. Evidence was presented in the form: AB→E; CA→E; AD→E; and so forth. In 1 story, all variables came from the same domain; in the other, the recurring candidate cause, A, came from a different domain (A was a psychological cause of a biological effect). After receiving this statistical evidence, children were asked to identify the cause of the effect on a new trial. Consistent with the predictions of a Bayesian model, all children were more likely to identify A as the cause within domains than across domains. Whereas 3.5-year-olds learned only from the within-domain evidence, 4- and 5-year-olds learned from the cross-domain evidence and were able to transfer their new expectations about psychosomatic causality to a novel task. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Powers of observation in creative design

Being perceptive is a trait highly valued in scientific and engineering professions. What a scientist or engineer notices while considering a problem, evaluating alternatives, or interpreting data has a profound impact on how a problem is viewed and solved. This paper focuses on processes we believe underlie being perceptive: firstly, preparation—becoming attuned to salient or important features; secondly, assimilation-detection and exploration of patterns (invariants) as well as anomalies; and thirdly, strategic control-heuristic strategies for exploring the implications of what has been observed. These processes play an integral role in characteristic activities within creative design, including problem reformulation, the emergence of properties and constraints on the solution, and the ability to incorporate into the design experimental feedback from the environment and from experiences with prototypes and previous designs. The paper presents a computational model incorporating these ideas, implemented in a system called IMPROVISER.     

An object-oriented product database using ROSE

A product database for a design and manufacturing enterprise serves as a repository for the data needed to manufacture a product. This paper discusses how an object-oriented persistent object manager named ROSE, under development at Rensselaer, has been used in the Rensselaer CIM program to develop a STEP/PDES-compliant product database for its research test bed. In so doing, it provides an overview of the requirements for a product database system in an integrated design and manufacturing facility, and it discusses how the chosen system meets these requirements.