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This research proposes a multivariate control chart, whose parameters are optimized using genetic algorithms (GA) in order to accelerate the detection of a change in the vector of means.

This chart is based on a variation of the Hotelling T² chart using a sampling scheme called generalized multiple dependent state sampling. For the analysis of performances of this chart, the out-of-control average run length (ARL) values were used for different scenarios. In this comparison, it was considered the classic Hotelling T² chart and the T² chart using the scheme called multiple dependent state sampling.

It was observed that the new chart with its optimized parameters is more efficient to detect an out-of-control process. Additionally, a sensitivity analysis was performed, and it was concluded that the best yields are obtained when the change to be considered in the optimization is small. An application in the resolution of a real problem is given.

In this research, a multivariate control chart is proposed based on the Hotelling T² statistic but adding a sampling scheme. This makes this control chart more efficient than the classic T² chart because the new chart not only uses the current information of the T² statistic but also conditions the decision to consider a process as “in- control” on the statistic's previous information. The practitioner can obtain the optimal parameters of this new chart through a friendly program developed by the authors.

I review the burgeoning literature on applications of Markov regime switching models in empirical finance. In particular, distinct attention is devoted to the ability of Markov Switching models to fit the data, filter unknown regimes and states on the basis of the data, to allow a powerful tool to test hypotheses formulated in light of financial theories, and to their forecasting performance with reference to both point and density predictions. The review covers papers concerning a multiplicity of sub-fields in financial economics, ranging from empirical analyses of stock returns, the term structure of default-free interest rates, the dynamics of exchange rates, as well as the joint process of stock and bond returns.

Longitudinal regression analysis is conducted to clarify causal relations and control for unwanted influences from actor heterogeneity and state dependence on theoretically important coefficient estimates. Because strategic management contains theory on how firms differ and how firm actions are influenced by their current strategic position and recent experiences, consistency of theory and methodology often requires use of longitudinal methods. We describe the theoretical motivation for longitudinal methods and outline some common methods. Based on a survey of recent articles in strategic management, we argue that longitudinal methods are now used more frequently than before, but the use is still inconsistent and insufficiently justified by theoretical or empirical considerations. In particular, strategic management researchers should use dynamic models more often, and should test for the presence of actor effects, autocorrelation, and heteroscedasticity before applying corrections.

I survey applications of Markov switching models to the asset pricing and portfolio choice literatures. In particular, I discuss the potential that Markov switching models have to fit financial time series and at the same time provide powerful tools to test hypotheses formulated in the light of financial theories, and to generate positive economic value, as measured by risk-adjusted performances, in dynamic asset allocation applications. The chapter also reviews the role of Markov switching dynamics in modern asset pricing models in which the no-arbitrage principle is used to characterize the properties of the fundamental pricing measure in the presence of regimes.

The purpose of this paper is to study the combined effect of human error, common‐cause failure, redundancy, and maintenance policies on the performance of a system composed of three‐state devices.

Generalized expressions for time‐dependent and steady state availability of a generalized maintainable three‐state device parallel system subjected to human errors and common‐cause failures are developed in the paper under two maintenance policies: Type I repair policy (i.e. only the completely failed system is repaired); and Type II repair policy (i.e. both partially and completely failed system is repaired). The Markov method is used to develop general and special case expressions for state probabilities, and system time‐dependent and steady state availabilities.

In the case of three‐state devices, it is demonstrated that by increasing the number of redundant devices in parallel do not necessarily lead to the improvement in the system availability. In fact, the availability of the system depends significantly on the dominant failure mode of the devices (i.e. short‐mode or open‐mode). When comparing the effect of maintenance policies on the system availability, it is observed that the Type II repair policy does not lead to an improvement in the system availability. Furthermore, it is observed that both human error and common‐cause failure independently lead to lower system availability.

This study will help maintenance engineers and reliability practitioners to become aware of the combined impact of redundancy, human error, common‐cause failure, and maintenance policies on the performance of the three‐state device systems. Consequently, they will make better maintenance related decisions in organizations such as oil refineries and power stations that use three state devices quite extensively.

Most of the past models have independently studied the effects of redundancy, human error, and common‐cause failure on maintainable system made up of three‐state devices. This effort is one of the first attempts to study the combined effects of all these factors in a parallel system composed of three state devices.

Although the use of data from different levels is very common in international marketing research, the practice of employing multi‐level analysis techniques is relatively new. The paper aims to provide an application of a specific case of multi‐level modelling – where the dependent variable is dichotomous, which is often the case in marketing research (e.g. whether a consumer buys the brand or not, whether he/she is aware of the brand or not, etc.)

A hierarchical generalized linear model is employed.

Since this is a technical paper, the authors would like to emphasize the process rather than the empirical findings. In summary, the paper: provides a brief theoretical overview of Hierarchical Linear Modeling and Hierarchical Generalized Linear Modeling; illustrates the application of the method using the domains of consumers within countries and a dichotomous dependent variable; focuses on interpretation of log‐odds results; and concludes with practical issues and research implications.

The main value of this research is to demonstrate how to employ multi‐level models when the dependent variable is dichotomous. Multi‐level techniques are quite new in international marketing research, although nested data structures are relatively common in our field. This is a technical paper that guides the researchers as to how to apply and interpret the results when modeling such data with a dichotomous dependent variable.

The purpose of this paper is to analyze the relationship between strategic assets and the launch of new products in technology-based incubators (TBIs) in Brazil.

The authors applied two surveys, one for the universe of TBIs’ managers in the state of Sao Paulo, Brazil, and the other to the incubated firms’ managers/owners. Two statistical techniques were used: correlation analysis and multiple linear regression.

The main finding of this paper is that TBIs’ strategies focusing on the supply of knowledge assets and the creation of relationship assets are more effective than strategies focused only on the supply of physical infrastructure for firms located in incubators.

Because the sample of 100 respondents of incubated companies was the result of a non-probabilistic convenience sampling, the outcomes also cannot be generalized.

For managers of TBIs, there is a challenge to focus on the supply of intangible and high value added assets for incubated firms. For managers/owners of incubated firms, the authors provide an orientation to what they should seek or demand when deciding where to place their business in a TBI. For the government, the results of this research may help to formulate public policies to support and incentivize TBIs. For investors, the results can help to define where to seek the most innovative projects.

Innovation and entrepreneurship are understood as sources of wealth creation and social development.

The authors propose in this paper that there is a theoretical gap between traditional theories of innovation and entrepreneurship and the strategic behavior and performance of business incubators and their interconnected stakeholders. Here the authors seek to bridge this gap.

The study investigates whether there is an association between climate types and farm risk attitudes of principal operators.

The study exploits temperature variation in the diverse climate types across the US and defines hot- and cold-climate states. Ordered logit and generalized ordered logit models are used to model principal operators' farm risk attitudes, which are measured on a Likert scale. The study uses two datasets. The first dataset is a 2017 survey of US large commercial producers (LCPs). The second dataset provides a Köppen-Geiger climate classification of the US at a spatial resolution of 5 arcmin for a 25-year period (1986–2010).

The study finds that principal operators in hot-climate states are 4–5% more likely to have a higher willingness to take farm risk compared to principal operators in cold-climate states.

It is likely that farm risk mitigation decisions differ between hot- and cold-climate states. For instance, the authors show that corn acres' enrollment in federal crop insurance and computers' usage for farm business are pursued more intensely in cold-climate states than in hot-climate states. A differentiation of farm risk attitude by hot- and cold-climate states may help agribusiness, the government and economists in their farm product offerings, farm risk management programs and agricultural finance models, respectively.

Based on Köppen-Geiger climate classification, the study introduces hot- and cold-climate concepts to understand the relationship between climate types and principal operators' farm risk attitudes.