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Differing from previous studies trying to solve the electromagnetic compatibility (EMC) issue by addressing single factor, this study aims to combine measures of shielding, filtering and grounding to design parameters with the Taguchi method at the beginning of product design to come up with the optimal parameter combination.

EMC-related performance such as radiated emission, conduction interference and electrical fast transient/burst immunity (EFT) are response variables, whereas the printed circuit board and mechanic design-relevant parameters are considered as control factors. The noise factors are peripherals used together with the tablet.

The optimal design parameter matrix based on results from the application and integration of multivariate analysis method of principal component grey relation and technique for order preference by similarity to ideal solution suggests 14 grounding screw holes, cooling aperture of casing at diameter of 3 mm and staggered layout and 300O filter located at source of noise. Validation of this matrix shows around 10, 1 and 8 per cent improvement in radiation, conduction interference and EFT immunity.

The multivariate quality parameters’ design method proposed by this study improves EMC characteristics of products and meets the design specification required by customer, accelerating electronic product research and development process and complying with electromagnetic interference test regulations set forth by individual country.

The purpose of this study is to use the Taguchi Method for parametric design in the early stages of product development. electromagnetic compatibility (EMC) issues can be considered in the early stages of product design to reduce counter-measure components, product cost and labor consumption increases due to a number of design changes in the R&D cycle and to accelerate the R&D process.

The three EMC characteristics, including radiated emission, conducted emission and fast transient impulse immunity of power, are considered response values; control factors are determined with respect to the relevant parameters for printed circuit board and mechanical design of the product and peripheral devices used in conjunction with the product are considered as noise factors. The optimal parameter set is determined by using the principal component gray relational analysis in conjunction with both response surface methodology and artificial neural network.

Market specifications and cost of components are considered to propose an optimal parameter design set with the number of grounded screw holes being 14, the size of the shell heat dissipation holes being 3 mm and the arrangement angle of shell heat dissipation holes being 45 degrees, to dispose of 390 O filters on the noise source.

The optimal parameter set can improve EMC effectively to accommodate the design specifications required by customers and pass test regulations.

This study aims to focus on the passive components of System in Package SiP modules and discusses the geometric pad designs for 01005-sized passive components, the front end design of the hole size and shape of the stencil and the parameters of the stencil sidewall coating, to determine the optimum parameter combination.

This study plans and conducts experiments, where a L8(27) inner orthogonal array is built to consider the control factors, including a L4(23) outer orthogonal array to consider the noise factor, and the experimental data are analyzed by using the technique for order preference by similarity to ideal solution multi-quality analysis method.

The results show that the optimum design parameter level combination is that the solder mask opening pad has no solder mask in the lower part of the component, the pad width is 1.1 times that of the component width, the pad length is 1.75 times that of the electrode tip length, the pad spacing is 5 mil, the stencil open area is 90% of the pad area, the stencil opening corner has a 3 mil chamfer angle, and the stencil sidewall is free of nano-coating.

The parameter design and multi-quality analysis method, as proposed in this study, can effectively develop the layout of passive components on a high-density SiP module substrate, to stabilize the process and increase the production yield.

This paper aims to discuss the key factors affecting the quality characteristics, such as the number of solder balls, the spread distance of residual underfill and the completion time of the underfilling.

The Taguchi method is applied to configure the orthogonal table and schedule and execute the experiment. In addition, principal components analysis is used to obtain the points. Then, based on gray relational analysis and the technique for order preference by similarity to ideal solution, the closeness between each quality characteristic and the ideal solution is adopted as the basis for evaluating the quality characteristics.

The optimal parameter combination is proposed, which includes 4 dispensing (11 mg/dispensing), a “half flow” interval state, 80°C preheating module PCB board and an L-shaped dispensing path and verification testing is performed.

For vehicles and handheld electronic products, solder joints that connect electronic components to printed circuit boards may be cracked due to collision, vibration or falling. Consequently, solder balls are closely surrounded and protected by the underfill to improve joint strength and resist external force factors, such as collision and vibration. This paper addresses the defects caused during the second reflow process of a vehicle electronic communication module after the underfilling process.

The purpose of this study is to propose an effective unsupervised one-class-classifier (OCC) support vector machine (SVM)-based single multivariate control chart (OCC-SVM) to simultaneously monitor “location” and “scale” shifts of a manufacturing process.

The step-by-step approach to developing, implementing and fine-tuning the intrinsic parameters of the OCC-SVM chart is demonstrated based on simulation and two real-life case examples.

A comparative study, considering varied known and unknown response distributions, indicates that the OCC-SVM is highly effective in detecting process shifts of samples with individual observations. OCC-SVM chart also shows promising results for samples with a rational subgroup of observations. In addition, the results also indicate that the performance of OCC-SVM is unaffected by the small reference sample size.

The sample responses are considered identically distributed with no significant multivariate autocorrelation between sample observations.

The proposed easy-to-implement chart shows satisfactory performance to detect an out-of-control signal with known or unknown response distributions.

Various multivariate (e.g. parametric or nonparametric) control chart(s) are recommended to monitor the mean (e.g. location) and variance (e.g. scale) of multiple correlated responses in a manufacturing process. However, real-life implementation of a parametric control chart may be complex due to its restrictive response distribution assumptions. There is no evidence of work in the open literature that demonstrates the suitability of an unsupervised OCC-SVM chart to simultaneously monitor “location” and “scale” shifts of multivariate responses. Thus, a new efficient OCC-SVM single chart approach is proposed to address this gap to monitor a multivariate manufacturing process with unknown response distributions.

The purpose of this paper is to provide an analysis of the dependence structure between returns from real estate investment trusts (REITS) and a stock market index. Further, the aim is to illustrate how copula approaches can be applied to model the complex dependence structure between the assets and for risk measurement of a portfolio containing investments in REIT and equity indices.

The usually suggested multivariate normal or variance‐ covariance approach is applied, as well as various copula models in order to investigate the dependence structure between returns of Australian REITS and the Australian stock market. Different models including the Gaussian, Student t, Clayton and Gumbel copula are estimated and goodness‐of‐fit tests are conducted. For the return series, both the Gaussian and a non‐parametric estimate of the distribution is applied. A risk analysis is provided based on Monte Carlo simulations for the different models. The value‐at‐risk measure is also applied for quantification of the risks for a portfolio combining investments in real estate and stock markets.

The findings suggest that the multivariate normal model is not appropriate to measure the complex dependence structure between the returns of the two asset classes. Instead, a model using non‐parametric estimates for the return series in combination with a Student t copula is clearly more suitable. It further illustrates that the usually applied variance‐covariance approach leads to a significant underestimation of the actual risk for a portfolio consisting of investments in REITS and equity indices. The nature of risk is better captured by the suggested copula models.

To the authors', knowledge, this is one of the first studies to apply and test different copula models in real estate markets. Results help international investors and portfolio managers to deepen their understanding of the dependence structure between returns from real estate and equity markets. Additionally, the results should be helpful for implementation of a more adequate risk management for portfolios containing investments in both REITS and equity indices.

The purpose of this paper is to assess price linkages and patterns of transmission among producer and consumer markets for apple in Slovenia.

Non-linear error correction models are applied. Non-linearities are allowed by means of threshold and multivariate local linear regression estimation techniques. Monthly prices over the period 2000-2011 are used in the empirical application.

Both techniques provide evidence of non-linearities in price adjustments. Findings suggest that producer and consumer prices tend to increase rather than decrease. Results also indicate that parametric threshold approaches may have difficulties in adequately representing price behavior dynamics.

The main contribution of this work to the literature relies on the fact that this is the first attempt to assess vertical price transmission in the apple sector in Central and Eastern European Country markets. Further, it is the first attempt to use multivariate local linear regression techniques in this context.

Applied econometric analysis is often performed using data collected from large-scale surveys. These surveys use complex sampling plans in order to reduce costs and increase the estimation efficiency for subgroups of the population. These sampling plans result in unequal inclusion probabilities across units in the population. The purpose of this paper is to derive the asymptotic properties of a design-based nonparametric regression estimator under a combined inference framework. The nonparametric regression estimator considered is the local constant estimator. This work contributes to the literature in two ways. First, it derives the asymptotic properties for the multivariate mixed-data case, including the asymptotic normality of the estimator. Second, I use least squares cross-validation for selecting the bandwidths for both continuous and discrete variables. I run Monte Carlo simulations designed to assess the finite-sample performance of the design-based local constant estimator versus the traditional local constant estimator for three sampling methods, namely, simple random sampling, exogenous stratification and endogenous stratification. Simulation results show that the estimator is consistent and that efficiency gains can be achieved by weighting observations by the inverse of their inclusion probabilities if the sampling is endogenous.

The purpose this paper is to review some of the statistical methods used in the field of social sciences.

A review of some of the statistical methodologies used in areas like survey methodology, official statistics, sociology, psychology, political science, criminology, public policy, marketing research, demography, education and economics.

Several areas are presented such as parametric modeling, nonparametric modeling and multivariate methods. Focus is also given to time series modeling, analysis of categorical data and sampling issues and other useful techniques for the analysis of data in the social sciences. Indicative references are given for all the above methods along with some insights for the application of these techniques.

This paper reviews some statistical methods that are used in social sciences and the authors draw the attention of researchers on less popular methods. The purpose is not to give technical details and also not to refer to all the existing techniques or to all the possible areas of statistics. The focus is mainly on the applied aspect of the techniques and the authors give insights about techniques that can be used to answer problems in the abovementioned areas of research.

The purpose of this paper is to understand the relationship between consumers and organic wine purchases and how this production process can affect their willingness to pay a premium price. The aim of this paper is, therefore, to assess the role of organic attributes on the consumer’s choice, and how consumer socio-demographic characteristics influence the premium price for organic wine.

This survey represents the continuation of a paper already published by the same authors, which was aimed at identifying the factors that influence the preference and consumption of wine. The information about wine consumers was collected by the use of an ad hoc questionnaire, administered near very busy places, (supermarkets, main squares, theatres, universities and municipal offices) in a random way, within several municipalities in the province of Messina (Italy). The questionnaire, which was anonymous, was distributed directly with the face-to-face method. In total, 1,097 subjects compose the obtained sample. Among these, 459 subjects (42%) said they consumed organic wine, while the remaining 638 (58%) said they did not consume it. In this research, the non-parametric combination test is used, based on a permutation test, chosen for the several optimal properties which characterize it. Permutation tests represent an effective solution for problems concerning the testing of multidimensional hypotheses that are difficult to face in a parametric context.

The results obtained may have interesting implications for wineries in the prediction and choice of the company to convert traditional production into organic production, to meet new markets and new segments of consumers. In this direction, without any doubt, organic wines represent an opportunity for the wineries.

Although field experiments are extremely useful for testing behavioural hypotheses, they are often limited by the small sample.

The originality of the research lies mainly in a deeper knowledge of organic wine consumption trends. The study carried out highlights how important it is to know which factors restrain consumers from buying wine so that companies can review their promotion and communication policies and, if possible, meet the needs of the market. The strength of this paper is the use of an adequate statistical instrument. This multivariate and multistrata procedure allows us to reach effective solutions concerning problems of multidimensional hypotheses verifying within the non-parametric permutation inference; it is used in different application fields that deal with the verifying of multidimensional hypotheses with a complexity that cannot be managed in a parametric context.