Does New Zealand visitors follow the Joseph Effect? Some empirical evidence

The report departs from conventional time series analysis and investigates the existence of long memory (LRD) in the stream of daily visitors, arriving from various sources to New Zealand from 1997 to 2010, using selected estimators of the Hurst-exponent. The daily arrivals of visitors are treated as a stream of "digital signals" with the inherent noise. After minimizing the noise (i.e. the presence of short-term trends, periodicities, and cycles) we found the existence of significant long memory embedded in our data of daily visitors from all sources and in the aggregate. Strong evidence of embedded “long memory” implies that Joseph Effect – that good times beget good times and bad times beget bad – whose existence in the underlying process may have interesting implications for tourism policy makers. Our findings suggest evidence of such long term memory in tourist arrival data. Further, unless this long memory effect is taken into consideration, any traditional statistical analysis based on Gaussian and Poisson assumptions may be overly biased

Evaluating a formative feedback intervention for international students

Assessment is too often concerned with measurement, rather than learning; however, there is a growing interest in research into formative assessment, which appears justified by studies into its effects on learning. Changes in higher education have led to increased numbers of students, many of whom are from non-traditional backgrounds. This has highlighted the need for transparency and student involvement in assessment. However, the corresponding pressures on staff and on resources mean that many desirable innovations are not easy to implement. The overall aim of this formative feedback intervention (FFI) was to provide timely and helpful feedback to international students who are final-year direct entrants in a large business school. Timeliness of feedback and the development of academic literacy were key concerns. The study concludes that although the FFI did not have a significant impact on module grades, the intervention was successful in getting students to engage in academic writing at an early stage. Most respondents perceived the feedback to be helpful and the feedback messages were clearly received and internalised. Whether appropriate actions were taken by the students to close the gap between their current and their target level requires further investigation

Dynamics of cancer recurrence

Mutation-induced drug resistance in cancer often causes the failure of therapies and cancer recurrence, despite an initial tumor reduction. The timing of such cancer recurrence is governed by a balance between several factors such as initial tumor size, mutation rates and growth kinetics of drug-sensitive and resistance cells. To study this phenomenon we characterize the dynamics of escape from extinction of a subcritical branching process, where the establishment of a clone of escape mutants can lead to total population growth after the initial decline. We derive uniform in-time approximations for the paths of the escape process and its components, in the limit as the initial population size tends to infinity and the mutation rate tends to zero. In addition, two stochastic times important in cancer recurrence will be characterized: (i) the time at which the total population size first begins to rebound (i.e., become supercritical) during treatment, and (ii) the first time at which the resistant cell population begins to dominate the tumor.Comment: Published in at http://dx.doi.org/10.1214/12-AAP876 the Annals of Applied Probability (http://www.imstat.org/aap/) by the Institute of Mathematical Statistics (http://www.imstat.org

Apartment Security: A Note on Gated Access and Rental Rates

This study empirically examines the dynamics of the private industrial market in Singapore using a Vector Error Correction Model (VECM), which is derived based on the theoretical framework of an extended accelerator investment model. The GDP in manufacturing sector (LMGDP) and the composite leading indicator (LCLI) were two unrestricted long-run forcing variables included in the VECM for the industrial space demand, together with a pre-determined error correction mechanism (ecm) and other determinants. The results of the VECM estimation showed negative effects of the changes in the manufacturing GDP (LMGDP) at different lags on the private industrial demand (LPRD). Three possible reasons are hypothesized for the negative manufacturing outputs and industrial space demand relationship. First, firms substitute space for other factors of production when the demand for their output increases. Second, firms take up more space than that required for the existing scale of production and the excess space can be converted to meet the production needs for the short-term surge in the outputs. A possible switch of demand from the private to the public industrial markets during a period of strong output growth may be the third contributory factor. In the generalized forecast error variance decomposition analysis, one-standard deviation shocks to the manufacturing GDP (LMGDP) was found to account for an average 67.10% of the variances of LPRD. However, in shorter terms of less than 15-period, the industrial demand own shocks appeared to be the most important determinant of the variation in industrial real estate demand. It was also found that the most volatile impulse responses from the industrial demand variance.

Multifocality and recurrence risk: a quantitative model of field cancerization

Primary tumors often emerge within genetically altered fields of premalignant cells that appear histologically normal but have a high chance of progression to malignancy. Clinical observations have suggested that these premalignant fields pose high risks for emergence of secondary recurrent tumors if left behind after surgical removal of the primary tumor. In this work, we develop a spatio-temporal stochastic model of epithelial carcinogenesis, combining cellular reproduction and death dynamics with a general framework for multi-stage genetic progression to cancer. Using this model, we investigate how macroscopic features (e.g. size and geometry of premalignant fields) depend on microscopic cellular properties of the tissue (e.g.\ tissue renewal rate, mutation rate, selection advantages conferred by genetic events leading to cancer, etc). We develop methods to characterize how clinically relevant quantities such as waiting time until emergence of second field tumors and recurrence risk after tumor resection. We also study the clonal relatedness of recurrent tumors to primary tumors, and analyze how these phenomena depend upon specific characteristics of the tissue and cancer type. This study contributes to a growing literature seeking to obtain a quantitative understanding of the spatial dynamics in cancer initiation.Comment: 36 pages, 11 figure