Inference for the two-parameter bathtub-shaped distribution based on record data

Here we consider the record data from the two-parameter of bathtub-shaped distribution. First, we develop simplified forms for the single moments, variances and covariance of records. These distributional properties are quite useful in obtaining the best linear unbiased estimators of the location and scale parameters which can be included in the model. The estimation of the unknown shape parameters and prediction of the future unobserved records based on some observed ones are discussed. Frequentist and Bayesian analyses are adopted for conducting the estimation and prediction problems. The likelihood method, moment based method, bootstrap methods as well as the Bayesian sampling techniques are applied for the inference problems. The point predictors and credible intervals of future record values based on an informative set of records can be developed. Monte Carlo simulations are performed to compare the so developed methods and one real data set is analyzed for illustrative purposes.     

Stochastic comparisons of coherent systems

The study of stochastic comparisons of coherent systems with different structures is a relevant topic in reliability theory. Several results have been obtained for specific distributions. The present paper is focused on distribution-free comparisons, that is, orderings which do not depend on the component distributions. Different assumptions for the component lifetimes are considered which lead us to different comparison techniques. Thus, if the components are independent and identically distributed (IID) or exchangeable, the orderings are obtained by using signatures. If they are just ID (homogeneous components), then ordering results for distorted distributions are used. In the general case or in the case of independent (heterogeneous) components, a similar technique based on generalized distorted distributions is applied. In these cases, the ordering results may depend on the copula used to model the dependence between the component lifetimes. Some illustrative examples are included in each case.     

The multiple filter test for change point detection in time series

A framework for the detection of change points in the expectation in sequences of random variables is presented. Specifically, we investigate time series with general distributional assumptions that may show an unknown number of change points in the expectation occurring on multiple time scales and that may also contain change points in other parameters. To that end we propose a multiple filter test (MFT) that tests the null hypothesis of constant expectation and, in case of rejection of the null hypothesis, an algorithm that estimates the change points.The MFT has three important benefits. First, it allows for general distributional assumptions in the underlying model, assuming piecewise sequences of i.i.d. random variables, where also relaxations with regard to identical distribution or independence are possible. Second, it uses a MOSUM type statistic and an asymptotic setting in which the MOSUM process converges weakly to a functional of a Brownian motion which is then used to simulate the rejection threshold of the statistical test. This approach enables a simultaneous application of multiple MOSUM processes which improves the detection of change points that occur on different time scales. Third, we also show that the method is practically robust against changes in other distributional parameters such as the variance or higher order moments which might occur with or even without a change in expectation. A function implementing the described test and change point estimation is available in the R package MFT.     

Hybrid estimators for small diffusion processes based on reduced data

We deal with the Bayes type estimators and the maximum likelihood type estimators of both drift and volatility parameters for small diffusion processes defined by stochastic differential equations with small perturbations from high frequency data. From the viewpoint of numerical analysis, initial Bayes type estimators for both drift and volatility parameters based on reduced data are required, and adaptive maximum likelihood type estimators with the initial Bayes type estimators, which are called hybrid estimators, are proposed. The asymptotic properties of the initial Bayes type estimators based on reduced data are derived and it is shown that the hybrid estimators have asymptotic normality and convergence of moments. Furthermore, a concrete example and simulation results are given.     

Extreme value statistics for censored data with heavy tails under competing risks

This paper addresses the problem of estimating, from randomly censored data subject to competing risks, the extreme value index of the (sub)-distribution function associated to one particular cause, in a heavy-tail framework. Asymptotic normality of the proposed estimator is established. This estimator has the form of an Aalen-Johansen integral and is the first estimator proposed in this context. Estimation of extreme quantiles of the cumulative incidence function is then addressed as a consequence. A small simulation study exhibits the performances for finite samples.     

Market-based capabilities and financial performance of firms: insights into marketing’s contribution to firm value

While there is recognition that market-based capabilities contribute to a firm’s financial performance, the exposition is largely conceptual (Srivastava et al. Journal of Marketing 62:2–18, 1998; Journal of Marketing 63:168–179, 1999). Using a resource based view of the firm, the present study proposes that (1) market-based assets and capabilities of a firm impacts (2) performance in three market-facing business processes (new product development, supply-chain and customer management), which in turn, influence (3) the firm’s financial performance. It develops related hypotheses and tests the framework empirically. The study also examines for the first time the interrelationship among the three business processes and their impact on the market value of firms. Further, the study examines the moderating influence of two organizational variables—size and age of the firm. Overall, the major contribution of the study is that it offers a process linkage between capabilities, process performance and financial performance. The results of this research will provide strategic insights to managers on optimal customer management, product development and supply chain strategies.

Evaluation of subsidiary marketing performance: combining process and outcome performance metrics

Issues in evaluating marketing performance and devising appropriate metrics for measurement have taken center stage in marketing thought and practice in recent years. We propose an empirical model that enables a multinational enterprise (MNE) to assess the marketing performance of its subsidiaries, taking into explicit consideration the fact that tactical actions by subsidiaries contribute to the creation of assets that can be harnessed for marketing outcomes. Thus, our model captures the asset creation abilities of marketing expenditures and also takes in to account the environmental differences of the context in which each MNE subsidiary operates. We evaluate comparative, overall, and process-level (creation of market assets and market yield) marketing performance in the context of multi-country operations. This simultaneous examination of marketing process and marketing outcome performance enables a global corporation to gain strategic, operational, and diagnostic insights into the performance of its subsidiaries. Our approach is empirically illustrated with an evaluation of the marketing performance of subsidiaries of a large global corporation.     

When do the stock market returns to new product preannouncements predict product performance? Empirical evidence from the U.S. automotive industry

Substantial research has examined how stock market reactions to marketing actions affect subsequent marketing decisions. However, prior research provides limited insights into whether abnormal stock returns to a marketing action actually predict the future performance resulting from that action. This study focuses on new product preannouncements (NPPAs) and investigates the relationship between short-term stock market returns to an NPPA and the post-launch new product performance under various industry and firm conditions. Findings based on a dynamic panel data analysis of 208 NPPAs in the U.S. automotive industry between 2001 and 2014 reveal that stock returns associated with an NPPA are not an appropriate forward-looking measure of future product performance. However, under specific conditions (i.e., when the preannouncement is specific, the preannounced new product has low innovativeness, the preannouncing firm has a high reputation and invests heavily in advertising, and the preannouncement environment is less competitive), abnormal stock returns to NPPAs actually predict the future performance of new products. Thus, this study extends the marketing–finance and innovation literature with its focus on the conditions that affect the predictive power of immediate stock returns for the future performance of new products.     

Augmenting the eye of the beholder: exploring the strategic potential of augmented reality to enhance online service experiences

Driven by the proliferation of augmented reality (AR) technologies, many firms are pursuing a strategy of service augmentation to enhance customers’ online service experiences. Drawing on situated cognition theory, the authors show that AR-based service augmentation enhances customer value perceptions by simultaneously providing simulated physical control and environmental embedding. The resulting authentic situated experience, manifested in a feeling of spatial presence, functions as a mediator and also predicts customer decision comfort. Furthermore, the effect of spatial presence on utilitarian value perceptions is greater for customers who are disposed toward verbal rather than visual information processing, and the positive effect on decision comfort is attenuated by customers’ privacy concerns.