Measuring the Effects of Film and Television on Tourism to Screen Locations: A Theoretical and Empirical Perspective

It is generally believed that consumption of film and television products can induce tourism to destinations featured. We review the literature relating to this, examine it from the perspective of mass communications theory and develop a general causal model to measure the influence of screen products on visitor numbers. The model is quantified by way of a survey at two popular UK destinations. Our conclusions are that the contributions of screen products to visitor numbers are fractional, diffuse and substantial. We further find that the magnitude of the effect differs greatly between destinations and that this is related to background causes independent of screen effects.     

New Frontiers in Tourist Behavior Research: Steps toward Causal Inference from Non-experimental Data

Over the last two decades the range of methods for analyzing causal relationships has been significantly extended. New approaches to the theory of causality rely on the concept of “intervention” instead of “association”. Under an axiomatic framework they elaborate the conditions for safe causal inference from non-experimental data. Inferred Causation (IC) Theory combines elements of graph theory, statistics, logic and artificial intelligence research in computer science. It is not limited to parametric models in need of quantitative (ratio or interval scaled) data, but also operates much more generally on the observed conditional independence relationships among a set of qualitative (categorical) observations. Causal inferences do not appear to be restricted to experimental data. This is particularly promising for research domains such as consumer or tourist behavior where data from controlled experiments on real markets are rare. A case example highlights the potential use of Inferred Causation methodology in tourism research. It aims at measuring the direct and indirect influences of destination loyalty, perceived service quality and satisfaction on the tourist's intention to repeat visit.     

Estimating Mann–Whitney‐type Causal Effects

Mann–Whitney‐type causal effects are generally applicable to outcome variables with a natural ordering, have been recommended for clinical trials because of their clinical relevance and interpretability and are particularly useful in analysing an ordinal composite outcome that combines an original primary outcome with death and possibly treatment discontinuation. In this article, we consider robust and efficient estimation of such causal effects in observational studies and clinical trials. For observational studies, we propose and compare several estimators: regression estimators based on an outcome regression (OR) model or a generalised probabilistic index (GPI) model, an inverse probability weighted estimator based on a propensity score model and two doubly robust (DR), locally efficient estimators. One of the DR estimators involves a propensity score model and an OR model, is consistent and asymptotically normal under the union of the two models and attains the semiparametric information bound when both models are correct. The other DR estimator has the same properties with the OR model replaced by a GPI model. For clinical trials, we extend an existing augmented estimator based on a GPI model and propose a new one based on an OR model. The methods are evaluated and compared in simulation experiments and applied to a clinical trial in cardiology and an observational study in obstetrics.     

Distributional inference

The making of statistical inferences in distributional form is conceptionally complicated because the epistemic 'probabilities' assigned are mixtures of fact and fiction. In this respect they are essentially different from 'physical' or 'frequency-theoretic' probabilities. The distributional form is so attractive and useful, however, that it should be pursued. Our approach is In line with Walds theory of statistical decision functions and with Lehmann's books about hypothesis testing and point estimation: loss functions are defined, risk functions are studied, unbiasedness and equivariance restrictions are made, etc. A central theme is that the loss function should be 'proper'. This fundamental concept has been explored by meteorologists, psychometrists, Bayesian statisticians, and others. The paper should be regarded as an attempt to reconcile various schools of statisticians. By accepting what we regard 88 good and useful in the various approaches we are trying to develop a nondogmatic approach.     

This is your brain on neuromarketing: reflections on a decade of research

ABSTRACT

In this commentary, we reflect on the last decade of research in the field of neuromarketing and present a schematic illustration of the basic process of a typical neuromarketing study. We then identify three critical points of interest in this illustration that have not received enough discussion in neuromarketing-relevant literature, and which we consider to be somewhat problematic. These are the dominance of event-based designs in neuromarketing, the potential of alternative modalities in neuromarketing and the current focus on reverse inference in neuromarketing. We argue that, taken together, these points have substantive implications for the development of a more reflective neuromarketing, which in turn has greater potential to make a positive impact on marketing knowledge, marketing practice and public perceptions of marketing activity in general.     

How to Measure and Draw Causal Inferences with Patent Scope

Abstract

This paper presents an easy-to-use measure of patent scope that is grounded both in patent law and in the practices of patent attorneys. We validate our measure by showing both that patent attorneys’ subjective assessments of scope agree with our estimates, and that the behavior of patenters is consistent with it. Using our validation exercise, we find that previous measures of patent scope (i.e., the number of patent classes, the number of citations made by future patents, and the number of claims in a patent) are uninformative or misleading. To facilitate drawing causal inferences with our measure, we show how it can be used to create an instrumental variable, patent examiner scope toughness, which we also validate. We then demonstrate the power of this instrument by examining standard-essential patents. We show that an (exogenous) diminishment of patent scope leads to patents being much less likely to be declared standard essential.     

Perceptions of Deception: Making Sense of Responses to Employee Deceit

In this research, we examine the effects that customer perceptions of employee deception have on the customers’ attitudes toward an organization. Based on interview, archival, and observational data within the international airline industry, we develop a model to explain the complex effects of perceived dishonesty on observer’s attitudes and intentions toward the airline. The data revealed three types of perceived deceit (about beliefs, intentions, and emotions) and three additional factors that influence customer intentions and attitudes: the players involved, the beneficiaries of the deceit, and the harm done by the perceived lie. We develop a model with specific propositions to guide organizations with respect to apparently deceitful behavior of their employees. Implications and directions for future research are provided, focusing on the question of whether organizations should consistently encourage honesty or train their employees to be effective liars.     

Reflections on Fourteen Cryptic Issues Concerning the Nature of Statistical Inference*

The present paper provides the original formulation and a joint response of a group of statistically trained scientists to fourteen cryptic issues for discussion, which were handed out to the public by Professor Dr. D.R. Cox after his Bernoulli Lecture 1997 at Groningen University.     

Statistical aspects of interpreting DNA profiling in legal cases

DNA profiling has become one of the most powerful forensic techniques that is used in criminal investigations to identify suspects. Moreover, scientific DNA evidence has become an important component in criminal trials. The interpretation of DNA evidence involves many statistical and probabilistic aspects, which have been the subject of fierce debates over past years. Many controversies are rooted in the differences between the so-called Likelihood Ratio approach to evidence interpretation and other methods. However, though interesting for the statistician and probability theorist, these discussions are obstructive for legal proceedings. Fortunately, many issues have by now been settled, but the ever-expanding possibilities keep on raising new discussion topics. This paper provides a brief overview of the statistical discussion, and addresses some of the most interesting issues in more detail.     

A simulation estimator for testing the time homogeneity of credit rating transitions

The measurement of credit quality is at the heart of the models designed to assess the reserves and capital needed to support the risks of both individual credits and portfolios of credit instruments. A popular specification for credit-rating transitions is the simple, time-homogeneous Markov model. While the Markov specification cannot really describe processes in the long run, it may be useful for adequately describing short-run changes in portfolio risk. In this specification, the entire stochastic process can be characterized in terms of estimated transition probabilities. However, the simple homogeneous Markovian transition framework is restrictive. We propose a test of the null hypotheses of time-homogeneity that can be performed on the sorts of data often reported. We apply the tests to 4 data sets, on commercial paper, sovereign debt, municipal bonds and S&P-rated Corporates. The results indicate that commercial paper looks Markovian on a 30-day time scale for up to 6 months; sovereign debt also looks Markovian (perhaps due to a small sample size); municipals are well-modeled by the Markov specification for up to 5 years, but could probably benefit from frequent updating of the estimated transition matrix or from more sophisticated modeling, and S&P Corporate ratings are approximately Markov over 3 transitions but not 4.