Toulouse-Lautrec in Catford: new information about 'La Chaine Simpson'

A note on Toulouse-Lautrec's visit in June 1896 to the Catford Cycle Track and the posters he produced afterwards: Cycle Michael and La Chaîne Simpson.

An inverse method for determining source characteristics for emergency response applications

Following a malicious or accidental atmospheric release in an outdoor environment it is essential for first responders to ensure safety by identifying areas where human life may be in danger. For this to happen quickly, reliable information is needed on the source strength and location, and the type of chemical agent released. We present here an inverse modelling technique that estimates the source strength and location of such a release, together with the uncertainty in those estimates, using a limited number of measurements of concentration from a network of chemical sensors considering a single, steady, ground-level source. The technique is evaluated using data from a set of dispersion experiments conducted in a meteorological wind tunnel, where simultaneous measurements of concentration time series were obtained in the plume from a ground-level point-source emission of a passive tracer. In particular, we analyze the response to the number of sensors deployed and their arrangement, and to sampling and model errors. We find that the inverse algorithm can generate acceptable estimates of the source characteristics with as few as four sensors, providing these are well-placed and that the sampling error is controlled. Configurations with at least three sensors in a profile across the plume were found to be superior to other arrangements examined. Analysis of the influence of sampling error due to the use of short averaging times showed that the uncertainty in the source estimates grew as the sampling time decreased. This demonstrated that averaging times greater than about 5min (full scale time) lead to acceptable accuracy.

Processes controlling atmospheric dispersion through city centres

We develop a process-based model for the dispersion of a passive scalar in the turbulent flow around the buildings of a city centre. The street network model is based on dividing the airspace of the streets and intersections into boxes, within which the turbulence renders the air well mixed. Mean flow advection through the network of street and intersection boxes then mediates further lateral dispersion. At the same time turbulent mixing in the vertical detrains scalar from the streets and intersections into the turbulent boundary layer above the buildings. When the geometry is regular, the street network model has an analytical solution that describes the variation in concentration in a near-field downwind of a single source, where the majority of scalar lies below roof level. The power of the analytical solution is that it demonstrates how the concentration is determined by only three parameters. The plume direction parameter describes the branching of scalar at the street intersections and hence determines the direction of the plume centreline, which may be very different from the above-roof wind direction. The transmission parameter determines the distance travelled before the majority of scalar is detrained into the atmospheric boundary layer above roof level and conventional atmospheric turbulence takes over as the dominant mixing process. Finally, a normalised source strength multiplies this pattern of concentration. This analytical solution converges to a Gaussian plume after a large number of intersections have been traversed, providing theoretical justification for previous studies that have developed empirical fits to Gaussian plume models. The analytical solution is shown to compare well with very high-resolution simulations and with wind tunnel experiments, although re-entrainment of scalar previously detrained into the boundary layer above roofs, which is not accounted for in the analytical solution, is shown to become an important process further downwind from the source.

Combinando estratégias para estimação de efeitos de tratamento

Uma ferramenta importante na avaliação de políticas econômicas é a estimação do efeito médio de um programa ou tratamento sobre uma variável de interesse. Em geral, a atribuição do tratamento aos potenciais participantes não é aleatória, o que pode causar viés de seleção quando desconsiderada. Uma maneira de resolver esse problema é supor que o econometrista observa um conjunto de características determinantes, a menos de um componente estritamente aleatório, da participação. Sob esta hipótese, existem na literatura estimadores semiparamétricos do efeito médio do tratamento que são consistentes e capazes de atingir, assintoticamente, o limite de e ciência semiparamétrico. Entretanto, nas amostras freqüentemente disponíveis, o desempenho desses métodos nem sempre é satisfatório. O objetivo deste trabalho é estudar como a combinação das duas estratégias pode produzir estimadores com melhores propriedades em amostras pequenas. Para isto, consideramos duas formas de integrar essas abordagens, tendo como referencial teórico a literatura de estimação duplamente robusta desenvolvida por James Robins e co-autores. Analisamos suas propriedades e discutimos por que podem superar o uso isolado de cada uma das técnicas que os compõem. Finalmente, comparamos, num exercício de Monte Carlo, o desempenho desses estimadores com os de imputação e reponderação. Os resultados mostram que a combinação de estratégias pode reduzir o viés e a variância, mas isso depende da forma como é implementada. Concluímos que a escolha dos parâmetros de suavização é decisiva para o desempenho da estimação em amostras de tamanho moderado.

NATIONWIDE ESTIMATES OF BLACKBIRDS AND STARLINGS

An estimated 538 million blackbirds and Starlings are found in the United States, based on the national cooperative blackbird/Starling winter roost survey conducted by the U.S. Fish and Wildlife Service during the 1974-75 winter period of December 20-February 15. Ap- proximately 74% or 398 million of these blackbirds and Starlings occurred in the Eastern States, including the tier from Minnesota to Louisiana; 26% or 139 million birds were in the West. The national roosting population in 1974-75 was composed of 11 species (Table 1) in the following approximate proportions: 38% Red-winged Blackbirds; 22% Common Grackles; 20% Starlings; 18% Brown-headed Cowbirds; 2% Brewer’s Blackbirds; and less than 1% six species combined (Rusty Blackbirds, Boat-tailed Grackles, Great-tailed Grackles, Tri-colored Black- birds, Yellow-headed Blackbirds, and Bronzed Cowbirds). (Some 2 million robins also were reported in the 1974-75 survey, though not solicited and therefore not tabulated, from 20 of the blackbird roosts in the Southeast.) The 1974-75 species proportions are similar to those found in the last nationwide winter survey (1969-70). In the 1963-64 national winter survey, Redwings made up 33% and Common Grackles 31% of the total population.

EXPERIMENTAL TREE TRIMMING TO CONTROL AN URBAN WINTER BLACKBIRD ROOST

Every fall millions of blackbirds come down the Mississippi Flyway to return to their winter roosts in Arkansas, Louisiana, and East Texas. When these roosts are located in urban areas, public pressure makes the more common chemical means of control impractical. A less destructive and more permanent method of control was sought. At Rice University, in Houston, Texas, there has been a blackbird roost of various sizes and durations since 1956. For the past two years we have had the opportunity both to study roosting blackbird biology and experiment with habitat alteration as a control method. This particular report concentrates on the results and interpretation of the tree- trimming program initiated in August 1974. The birds involved are primarily Brown-headed Cowbirds (Molothrus ater), along with Starlings (sturnus vulgaris), Common and Great-tailed Grackles (Quiscalus quiscula and Cassidix mexicanus), Red-winged Blackbirds (Agelaius phoenicus) and Robins (Turdus migratorius). The campus comprises 121 ha and was planted with live oaks (Quercus virginiana) in 1912. These trees retain their foliage throughout the winter and now form a closed canopy over some 5-6 ha. In the 60s and early 70s most of the birds that came to Houston for the winter roosted in a 64-ha woodlot 10 km north of campus. In January 1970, the U.S. Fish and Wildlife Roosting Survey reported one million birds at this site we call the North Loop. Fifteen- thousand birds were estimated at Rice.

The phenomenological experiences of Autobiographical Memory: A cross-sectional and a longitudinal study

Phenomenology is a critical component of autobiographical memory retrieval. Some memories are vivid and rich in sensory details whereas others are faded; some memories are experienced as emotionally intense whereas others are not. Sutin and Robins (2007) identified 10 dimensions in which a memory may vary—i.e., Vividness, Coherence, Accessibility, Sensory Details, Emotional Intensity, Visual Perspective, Time Perspective, Sharing, Distancing, and Valence—and developed a comprehensive psychometrically sound measure of memory phenomenology, the Memory Experiences Questionnaire (MEQ). Phenomenology has been linked to underlining stable dispositions—i.e. personality, as well as to a variety of positive/negative psychological outcomes—well-being and life satisfaction, depression and anxiety, among others. Using the MEQ, a cross-sectional and a longitudinal study were conducted on a large sample of American and Italian adults. In both studies, participants retrieved two ‘key’ personal memories, a Turning Point and a Childhood Memory, and rated the affect and phenomenology of each memory. Participants also completed self-reported measures of personality (i.e. Neuroticism and Conscientiousness), and measures of depression, well-being and life satisfaction. The present research showed that phenomenological ratings tend (a) to cross-sectionally increase across adulthood (Study 1), and (b) to be moderately stable over time, regardless the contents of the memories (Study 2). Interrelations among memory phenomenology, personality and psychological outcome variables were also examined (Study 1 and Study 2). In particular, autobiographical memory phenomenology was proposed as a dynamic expression of personality functioning that partially explains adaptive/maladaptive psychological outcomes. In fact, the findings partially supported the hypothesized mediating effect of phenomenology on the personality association with psychological outcomes. Implications of the findings are discussed proposing future lines of research. In particular, the need for more longitudinal studies is highlighted, along with the combined application of both self-report questionnaires and narrative measures.

Large Sample Theory for Semiparametric Regression Models with Two-Phase, Outcome Dependent Sampling

Outcome-dependent, two-phase sampling designs can dramatically reduce the costs of observational studies by judicious selection of the most informative subjects for purposes of detailed covariate measurement. Here we derive asymptotic information bounds and the form of the efficient score and influence functions for the semiparametric regression models studied by Lawless, Kalbfleisch, and Wild (1999) under two-phase sampling designs. We show that the maximum likelihood estimators for both the parametric and nonparametric parts of the model are asymptotically normal and efficient. The efficient influence function for the parametric part aggress with the more general information bound calculations of Robins, Hsieh, and Newey (1995). By verifying the conditions of Murphy and Van der Vaart (2000) for a least favorable parametric submodel, we provide asymptotic justification for statistical inference based on profile likelihood.

Estimation with Interval Censored Data in Longitudinal Studies

In biostatistical applications interest often focuses on the estimation of the distribution of a time-until-event variable T. If one observes whether or not T exceeds an observed monitoring time at a random number of monitoring times, then the data structure is called interval censored data. We extend this data structure by allowing the presence of a possibly time-dependent covariate process that is observed until end of follow up. If one only assumes that the censoring mechanism satisfies coarsening at random, then, by the curve of dimensionality, typically no regular estimators will exist. To fight the curse of dimensionality we follow the approach of Robins and Rotnitzky (1992) by modeling parameters of the censoring mechanism. We model the right-censoring mechanism by modeling the hazard of the follow up time, conditional on T and the covariate process. For the monitoring mechanism we avoid modeling the joint distribution of the monitoring times by only modeling a univariate hazard of the pooled monitoring times, conditional on the follow up time, T, and the covariates process, which can be estimated by treating the pooled sample of monitoring times as i.i.d. In particular, it is assumed that the monitoring times and the right-censoring times only depend on T through the observed covariate process. We introduce inverse probability of censoring weighted (IPCW) estimator of the distribution of T and of smooth functionals thereof which are guaranteed to be consistent and asymptotically normal if we have available correctly specified semiparametric models for the two hazards of the censoring process. Furthermore, given such correctly specified models for these hazards of the censoring process, we propose a one-step estimator which will improve on the IPCW estimator if we correctly specify a lower-dimensional working model for the conditional distribution of T, given the covariate process, that remains consistent and asymptotically normal if this latter working model is misspecified. It is shown that the one-step estimator is efficient if each subject is at most monitored once and the working model contains the truth. In general, it is shown that the one-step estimator optimally uses the surrogate information if the working model contains the truth. It is not optimal in using the interval information provided by the current status indicators at the monitoring times, but simulations in Peterson, van der Laan (1997) show that the efficiency loss is small.

Locally Efficient Estimation with Current Status Data and High Dimensional Covariates

In biostatistical applications, interest often focuses on the estimation of the distribution of time T between two consecutive events. If the initial event time is observed and the subsequent event time is only known to be larger or smaller than an observed monitoring time, then the data is described by the well known singly-censored current status model, also known as interval censored data, case I. We extend this current status model by allowing the presence of a time-dependent process, which is partly observed and allowing C to depend on T through the observed part of this time-dependent process. Because of the high dimension of the covariate process, no globally efficient estimators exist with a good practical performance at moderate sample sizes. We follow the approach of Robins and Rotnitzky (1992) by modeling the censoring variable, given the time-variable and the covariate-process, i.e., the missingness process, under the restriction that it satisfied coarsening at random. We propose a generalization of the simple current status estimator of the distribution of T and of smooth functionals of the distribution of T, which is based on an estimate of the missingness. In this estimator the covariates enter only through the estimate of the missingness process. Due to the coarsening at random assumption, the estimator has the interesting property that if we estimate the missingness process more nonparametrically, then we improve its efficiency. We show that by local estimation of an optimal model or optimal function of the covariates for the missingness process, the generalized current status estimator for smooth functionals become locally efficient; meaning it is efficient if the right model or covariate is consistently estimated and it is consistent and asymptotically normal in general. Estimation of the optimal model requires estimation of the conditional distribution of T, given the covariates. Any (prior) knowledge of this conditional distribution can be used at this stage without any risk of losing root-n consistency. We also propose locally efficient one step estimators. Finally, we show some simulation results.

Locally Efficient Estimation with Bivariate Right Censored Data

Estimation for bivariate right censored data is a problem that has had much study over the past 15 years. In this paper we propose a new class of estimators for the bivariate survival function based on locally efficient estimation. We introduce the locally efficient estimator for bivariate right censored data, present an asymptotic theorem, present the results of simulation studies and perform a brief data analysis illustrating the use of the locally efficient estimator.