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The purpose of this paper is to contribute to the literature in three ways: first, the authors investigate the impact of the sampling errors on optimal portfolio weights and on financial investment decision. Second, the authors advance a comparative analysis between various domestic and international diversification strategies to define a stochastic optimal choice. Third, the authors propose a new methodology combining the re-sampling method, stochastic optimization algorithm, and nonparametric stochastic dominance (SD) approach to analyze a stochastic optimal portfolio choice for risk-averse American investors who care about benefits of domestic diversification relative to international diversification. The authors propose a new portfolio optimization model involving SD constraints on the portfolio return rate. The authors define a portfolio with return dominating the benchmark portfolio return in the second-order stochastic dominance (SSD) and having maximum expected return. The authors combine re-sampling procedure and stochastic optimization to establish more flexibility in the investment decision rule.

The authors apply the re-sampling procedure to consider the sampling error in the optimization process. The authors try to resolve the problem of the stochastic optimal investment strategy choice using the nonparametric SD test by Linton et al. (2005) based on sub-sampling simulated p values. The authors apply the stochastic portfolio optimization algorithm with SSD constraints to define optimal diversified portfolios beating benchmark indices.

First, the authors find that reducing sampling error increases the dominance relationships between different portfolios, which, in turn, alters portfolio investment decisions. Though international diversification is preferred in some cases, the study’s results show that for risk-averse US investors, in general, there is no difference between the diversification strategies; this implies that there is no increase in the expected utility of international diversification for the period before and after the 2007-2008 financial crisis. Nevertheless, the authors find that stochastic diversification in domestic, global, and Europe, Australasia, and Far East markets delivers better risk returns for the US risk averters during the crisis period.

The originality of the idea in this paper is to introduce a new methodology combining the concept of portfolio re-sampling, stochastic portfolio optimization with SSD constraints, and the nonparametric SD test by Linton et al. (2005) based on subsampling simulated p values to analyze the impact of sampling errors on optimal portfolio returns and to investigate the problem of stochastic optimal choice between international and domestic diversification strategies. The authors try to prove more coherence in the portfolio choice with the stochastically and the uncertainty characters of the paper.

The deviation of process output is usually estimated through the range or deviation of the sub‐samples in the calculation of process capability indices (PCI) in quality management. There are autocorrelation between the adjacent data in many practical process outputs. But this influence on PCI is not considered in process capability analysis. The autocorrelation of output data can be represented with time‐series model. Based on the several stationary stochastic process models of the time‐series analysis, the impact of the sampling method, sub‐sample size and deviation estimating methods in standard statistical process control are analyzed via simulation for the processes with autocorrelation. The autocorrelation testing of process should be undertaken in these cases. The proper analysis methods are set up in the paper.

This study uses stochastic dominance (SD) theory to examine whether the traditional festival, such as the Spring Festival (often in February), affects the patterns of monthly anomaly for the Taiwan Stock Exchange (TWSE). The paper aims to discuss these issues.

The authors employ a new bootstrap-based test due to Linton, Maasoumi and Whang (hereafter LMW). The LMW test is well suited for financial time series data, such as monthly returns of various portfolios in this study, because it allows for general dependence among the prospects (distributions) and does not require the observations to be identically and independently distributed.

The particular findings of this study are that the February effect and the February-size effect indeed exist in the TWSE. Furthermore, allowing part of investors' assets is invested in the risky asset and the remaining part in a risk-free asset, first finding for monthly anomaly in the extant literature, is useful in distinguishing the performance among various size-month portfolios.

Instead of tax-loss and window dressing hypothesis, the Spring Festival money movement hypothesis can be used to well explain the findings.

The purpose of this paper is to focus on the case where – by design – one needs to impute cross-country cross-survey (CCCS) data (situation typical for example among multinational firms who are confronted with the need to carry out comparative marketing surveys with respondents located in several countries). Importantly, while some work demonstrates approaches for single-item direct measures, no prior research has examined the common situation in international marketing where the researcher needs to use multi-item scales of latent constructs. The paper presents problem areas related to the choices international marketers have to make when doing cross-country/cross-survey research and provides guidance for future research.

Multi-country sample of real data is used as an example of cross-sample imputation (292 New Zealand exporters and 302 Finnish ones) the international entrepreneurial orientation (IEO) data. Three variations of the input data are tested: first, imputation based on all the data available for the measurement model; second, imputation based on the set of items based on the invariance structure of the joint items shared across the two groups; and third, imputation based both on examination of the invariance structures of the joint items and the performance of the measurement model in the group where the full data was originally available.

Based on distribution comparisons imputation for New Zealand after completing the measurement model with Finnish data (Model C) gave the most promising results. Consequently, using knowledge on between country measurement qualities may improve the imputation results, but this benefit comes with a downside since it simultaneously reduces the amount of data used for imputation. None of the imputation models leads to the same statistical inferences about covariances between latent constructs than as the original full data, however.

Considering multiple imputation, the present exploratory study suggests that there are several concerns and issues that should be taken into account when planning CCCSs (or split questionnaire or sub-sampling designs). Even if there are several advantages available for well-implemented CCCS designs such as shorter questionnaires and improved response rates, these concerns lead us to question the appropriateness of the CCCS approach in general, due to the need to impute across the samples.

The combination of cross-country and cross-survey approaches is novel to international marketing, and it is not known how the different procedures utilized in imputation affect the results and their validity and reliability. The authors demonstrate the consequences of the various imputation strategy choices taken by using a real example of a two-country sample. The exploration may have significant implications to international marketing researchers and the paper offers stimulus for further research in the area.

The purpose of this paper is to use a stochastic dominance test to examine the relative performance of value vs growth stocks based on multiple value-growth proxies in the Taiwan stock market.

This work examines whether the return distribution of a value portfolio stochastically dominates that of a growth portfolio using a test proposed by Linton et al. (2005).

By applying stochastic dominance analysis on the full-sample period, the sub-sample period and the state of the world’s economic conditions, the authors find that the earnings-to-price or dividend-to-price ratio is better than the book-to-market ratio as a value-growth proxy in Taiwan. There are robust results even after adjusting for data frequency, a sampling method and sample excluding financial services.

This study makes the first attempt to examine value vs growth strategies based on multiple value-growth proxies in the emerging market of Taiwan by administering the stochastic dominance test.

New applications of solid freeform fabrication (SFF) are arising, such as functional rapid prototyping and in situ fabrication, which push SFF to its limits in terms of geometrical fidelity due to the applications' inherent process uncertainties. Current closed‐loop feedback control schemes monitor and manipulate SFF techniques at the process level, e.g. envelope temperature, feed rate. “Closing the loop” on the process level, instead of the overall part geometry level, leads to limitations in the types of errors that can be detected and corrected. The purpose of this paper is to propose a technique called greedy geometric feedback (GGF) control which “closes the loop” on the overall part geometry level.

The overall part geometry is monitored throughout the print and, using a greedy algorithm, real‐time decisions are made to serially determine the locations of subsequent droplets, i.e. overall part geometry is directly manipulated. A computer simulator and a physical experimental platform were developed to compare the performance of GGF to an open‐loop control scheme. Root mean square surface height errors were measured under controlled uncertainties in droplet height, droplet radius of curvature, droplet positioning and mid‐print part deformations.

The GGF technique outperformed open‐loop control under process uncertainties in droplet shape, droplet placement and mid‐print part deformations. The disparity between performances is dependant on the nature and extent of the imposed process uncertainties.

Future research will focus on improving the performance of GGF for specific cases by designing more complex greedy algorithmic scoring heuristics. Also, the technique will be generalized beyond heightmap representations of 3D spaces.

The GGF technique is the first to “close the loop” on the overall part geometry level. GGF, therefore, can compensate for a broader range of errors than existing closed‐loop feedback control schemes. Also, since the technique only requires the real‐time update of a very limited set of heights, the technique is computationally inexpensive and widely applicable. By developing a closed‐loop feedback scheme that addressed part geometry‐level errors, SFF can be applied to more challenging in situ fabrication scenarios with less conventional materials.

The purpose of this paper is to demonstrate the acceptable performance by using the limited input range towards lower open-loop DC gain operational amplifier (op-amp) of an 8-bit pipelined analog-to-digital converter (ADC) for mobile communication application.

An op-amp with folded cascode configuration is designed to provide the maximum open-loop DC gain without any gain-boosting technique. The impact of low open-loop DC gain is observed and analysed through the results of pre-, post-layout simulations and measurement of the ADC. The fabrication process technology used is Silterra 0.18-µm CMOS process. The silicon area by the ADC is 1.08 mm².

Measured results show the differential non-linearity (DNL) error, integral non-linearity (INL) error, signal-to-noise ratio (SNR) and spurious-free dynamic range (SFDR) are within −0.2 to +0.2 LSB, −0.55 LSB for 0.4 Vpp input range, 22 and 27 dB, respectively, with 2 MHz input signal at the rate of 64 MS/s. The static power consumption is 40 mW with a supply voltage of 1.8 V.

The experimental results of ADC showed that by limiting the input range to ±0.2 V, this ADC is able to give a good reasonable performance. Open-loop DC gain of op-amp plays a critical role in ADC performance. Low open-loop DC gain results in stage-gain error of residue amplifier and, thus, leads to nonlinearity of output code. Nevertheless, lowering the input range enhances the linearity to ±0.2 LSB.

The purpose of this paper is to examine the magnitude of country, industry and firm‐specific effects for firms competing in emerging economies and also explore differences between high and low performers.

The authors use ANOVA methodologies on samples from firms competing in Latin America between 1990‐2006.

It was found that the firm‐specific effect is the most important one, and relatively equivalent in magnitude to the firm‐specific effects found in developed countries. Country and industry effects are less important than the firm‐specific effect. Contrary to previous studies that indicate that the country effect is relatively more important in emerging economies, the authors found that it is even less important than the industry effect, a result that has important implications for strategic management and international business theory. The source behind the strong firm‐specific effects might stem from their resources and capabilities to manage and take advantage of the institutional and macroeconomic environments. Further analysis indicates that the firm‐specific effect is relatively more important for firms showing high performance than for those firms showing low performance.

Through these findings the authors feel that further research is needed so as to arm future managers with a more clear and comprehensive strategy when doing business in a Latin American country. The paper's findings are specific for large public corporations in Latin America.

The paper allows managers to think about sources of competitive advantages in emerging economies.

The paper shows that, despite weak institutional contexts and highly volatile macroeconomic environments, managers in the region should be able to obtain substantial differences in economic performances within the region. Activities needed for such differentiation might differ from those carried out in developed countries, with more emphasis on managing institutional voids and periods of economic and political cycles but the result should be the same.

Interturn winding faults, one of the most important causes of power transformers failures, cannot be detected by existing detection methods until they develop into high‐level faults with more severe damage to the transformer. The purpose of this paper is to describe development of a new discrete wavelet transform (DWT) based approach for detection of winding interturn faults.

The following approach was accomplished for development of the proposed fault detection method in this study. The DWT was first applied to decompose the terminal current signals of a transformer, which in turn were obtained from simulations using a finite elements method model of the transformer, into a series of wavelet components. Based on the characteristic features associated with interturn faults extracted from the decomposed waveforms of the terminal currents, a detection scheme was developed. An experimental setup was used to validate the proposed detection method.

The results of this study demonstrate the efficacy of DWT applied on terminal currents of the transformer to identify interturn faults on the windings well before such faults lead to a catastrophic failure. It is believed that, based on the present findings, there definitely exists scope for improving interturn fault diagnosis with wavelet transform.

Performing more detailed studies to find all relevant characteristics of the wavelet transform in this application, identifying the location of the faulted turns along winding, applying the method for indicating early stages of turn insulation deterioration and evaluating other type of wavelets for this application would be some future directions of this research.

With the proposed method, it is becoming possible to detect early signs of the fault occurrence, so that the necessary corrective actions can be taken to prevent long‐lasting outages and reduce down times of the faulty power transformer. The method will be particularly useful as a complement for the classical protection devices of the power transformers.

Some recent studies have been carried out regarding the application of DWT for discrimination between an internal fault and other disturbances such as magnetizing inrush and external faults. This paper extends those studies for the detection of interturn faults using more quantitative and qualitative characteristics features.

The goal of this work is to create a computational finite element model to perform thermoelastic stress analysis (TSA) with the usage of a non-ideal load frequency, containing the effects of the material thermal properties.

Throughout this document, the methodology of the model is presented first, followed by the procedure and results. The last part is reserved to results, discussion and conclusions.

This work had the main goal to create a model to perform TSA with the usage of non-ideal loading frequencies, considering the materials’ thermal properties. Loading frequencies out of the ideal range were applied and the model showed capable of good results. The created model reproduced acceptably the TSA, with the desired conditions.

This work creates a model to perform TSA with the usage of non-ideal loading frequencies, considering the materials’ thermal properties.