Abstract: Examining the daily Dow Jones Industrial Average (DJI) we find evidence both of higher-order anomalies and predictability. While most researchers are only aware of the relatively harmless anomalies that occur just in the mean, the first part of this article provides empirical evidence of more dangerous kinds of anomalies occurring in higher-order moments. This evidence casts some doubt on the common practice of fitting standard time series models (e.g., ARMA models, GARCH models, or stochastic volatility models) to financial time series and carrying out tests based upon autocorre lation coefficients without making proper provision for these anomalies. The second part of this article provides evidence in favor of the predictability of the returns on the DJI and, more interestingly, against the efficient market hypothesis. The special value of this evidence is due to the simplicity of the involved methods.
Abstract: We explore the possibility of modeling clustered count data using the Poisson Inverse Gaussian distribution. We develop a regression model, which relates the number of mastitis cases in a sample of dairy farms in Ontario, Canada, to various farm level covariates, to illustrate the method ology. Residual plots are constructed to explore the quality of the fit. We compare the results with a negative binomial regression model using max imum likelihood estimation, and to the generalized linear mixed regression model fitted in SAS.
Abstract: Frailty models have become popular in survival analysis for deal ing with situations where groups of observations are correlated. If the data comprise only exact or right-censored failure times, inference can be done by either integrating out the frailties directly or by using the EM algorithm. If there is both left- and right-censoring this is no longer the case. How ever the MCMC method of Clayton (1991, Biometrics 47, 467-485) can be easily extended by imputation of the left-censored times. Several schemes for doing this are suggested and compared. Application of the methods is illustrated using data on the joint failures of patients with fibrodysplasia ossificans progressiva.
Abstract: Conservation of artifacts is a major concern of museum cura tors. Light, humidity, and air pollution are responsible for the deterioration of many artifacts and materials. We present here an exploratory analysis of humidity and temperature data that were collected to document the en vironment of the Bowdoin College Museum of Art, located in the Walker Art Building at Bowdoin College. As a result of this study, funds are being sought to install a climate control system.
Abstract: The probability of winning a game in major league baseball depends on various factors relating to team strength including the past per formance of the two teams, the batting ability of the two teams and the starting pitchers. These three factors change over time. We combine these factors by adopting contribution parameters, and include a home field ad vantage variable in forming a two-stage Bayesian model. A Markov chain Monte Carlo algorithm is used to carry out Bayesian inference and to sim ulate outcomes of future games. We apply the approach to data obtained from the 2001 regular season in major league baseball.
Abstract: In this work we present a combined approach to contingency tables analysis using correspondence analysis and log-linear models. Several investigators have recognized relations between the aforementioned method ologies, in the past. By their combination we may obtain a better under standing of the structure of the data and a more favorable interpretation of the results. As an application we applied both methodologies to an epi demiological database (CARDIO2000) regarding coronary hert disease risk factors.a
Abstract: This paper introduces a visualization technique, SEER, devel oped for policy makers and researchers to graphically analyze and explore massive amounts of categorical data collected in longitudinal surveys. This technique (a) produces panels of graphs for multiple group analysis, where the groups do not have to be mutually exclusive, (b) profiles change pat terns observed in longitudinal data, and (c) clusters data into groups to enable policy makers or researchers to observe the factors associated with the changing patterns. This paper also includes the hash function, of the SEER method, expressed in matrix notation for it to be implemented across computer packages. The SEER technique is illustrated by using a national survey, the Survey of Doctorate Recipients (SDR), administered by the Na tional Science Foundation (NSF). Occupational changes and career paths for a panel sample of 14,901 doctorate recipients are profiled and discussed. Results indicated that doctorate recipients in some science and engineering fields are roughly two times more likely to work in an occupation when it is the discipline in which they received their doctorates.