A BAYESIAN HIERARCHICAL MODEL FOR CONSTRAINED DISTRIBUTED LAG FUNCTIONS: ESTIMATING THE TIME COURSE OF HOSPITALIZATION ASSOCIATED WITH AIR POLLUTION EXPOSURE

Numerous time series studies have provided strong evidence of an association between increased levels of ambient air pollution and increased levels of hospital admissions, typically at 0, 1, or 2 days after an air pollution episode. An important research aim is to extend existing statistical models so that a more detailed understanding of the time course of hospitalization after exposure to air pollution can be obtained. Information about this time course, combined with prior knowledge about biological mechanisms, could provide the basis for hypotheses concerning the mechanism by which air pollution causes disease. Previous studies have identified two important methodological questions: (1) How can we estimate the shape of the distributed lag between increased air pollution exposure and increased mortality or morbidity? and (2) How should we estimate the cumulative population health risk from short-term exposure to air pollution? Distributed lag models are appropriate tools for estimating air pollution health effects that may be spread over several days. However, estimation for distributed lag models in air pollution and health applications is hampered by the substantial noise in the data and the inherently weak signal that is the target of investigation. We introduce an hierarchical Bayesian distributed lag model that incorporates prior information about the time course of pollution effects and combines information across multiple locations. The model has a connection to penalized spline smoothing using a special type of penalty matrix. We apply the model to estimating the distributed lag between exposure to particulate matter air pollution and hospitalization for cardiovascular and respiratory disease using data from a large United States air pollution and hospitalization database of Medicare enrollees in 94 counties covering the years 1999-2002.

Bayesian Hierarchical Distributed Lag Models for Summer Ozone Exposure and Cardio-Respiratory Mortality

In this paper, we develop Bayesian hierarchical distributed lag models for estimating associations between daily variations in summer ozone levels and daily variations in cardiovascular and respiratory (CVDRESP) mortality counts for 19 U.S. large cities included in the National Morbidity Mortality Air Pollution Study (NMMAPS) for the period 1987 - 1994. At the first stage, we define a semi-parametric distributed lag Poisson regression model to estimate city-specific relative rates of CVDRESP associated with short-term exposure to summer ozone. At the second stage, we specify a class of distributions for the true city-specific relative rates to estimate an overall effect by taking into account the variability within and across cities. We perform the calculations with respect to several random effects distributions (normal, t-student, and mixture of normal), thus relaxing the common assumption of a two-stage normal-normal hierarchical model. We assess the sensitivity of the results to: 1) lag structure for ozone exposure; 2) degree of adjustment for long-term trends; 3) inclusion of other pollutants in the model;4) heat waves; 5) random effects distributions; and 6) prior hyperparameters. On average across cities, we found that a 10ppb increase in summer ozone level for every day in the previous week is associated with 1.25 percent increase in CVDRESP mortality (95% posterior regions: 0.47, 2.03). The relative rate estimates are also positive and statistically significant at lags 0, 1, and 2. We found that associations between summer ozone and CVDRESP mortality are sensitive to the confounding adjustment for PM_10, but are robust to: 1) the adjustment for long-term trends, other gaseous pollutants (NO_2, SO_2, and CO); 2) the distributional assumptions at the second stage of the hierarchical model; and 3) the prior distributions on all unknown parameters. Bayesian hierarchical distributed lag models and their application to the NMMAPS data allow us estimation of an acute health effect associated with exposure to ambient air pollution in the last few days on average across several locations. The application of these methods and the systematic assessment of the sensitivity of findings to model assumptions provide important epidemiological evidence for future air quality regulations.

Determination of extreme floods using a distributed hydrological model

Hydrological models developed for extreme precipitation of PMP type are difficult to calibrate because of the scarcity of available data for these events. This article presents the process and results of calibration for a distributed hydrological model at fine scale developed for the estimation of probable maximal floods in the case of a PMP. This calibration is done on two Swiss catchments for two events of summer storms. The calculation done is concentrated on the estimation of the parameters of the model, divided in two parts. The first is necessary for the computation of flow speeds while the second is required for the determination of the initial and final infiltration capacities for each terrain type. The results, validated with the Nash equation show a good correlation between the simulated and observed flows. We also apply this model on two Romanian catchments, showing the river network and estimated flow.

Global Distributed Delivery Model for Internet Service Provider

Globaalin talouden rakenteet muuttuvat jatkuvasti. Yritykset toimivat kansainvälisillä markkinoilla aiempaa enemmän. Tuotannon lisäämiseksi monet yritykset ovat ulkoistaneet tuotteidensa tuki- ja ylläpitotoiminnot halvan työvoiman maihin. Yritykset voivat tällöin keskittää toimintansa ydinosamiseensa. Vapautuneita resursseja voidaan käyttää yrityksen sisäisessä tuotekehityksessä ja panostaa seuraavan sukupolven tuotteiden ja teknologioiden kehittämiseen. Diplomityö esittelee Globaalisti hajautetun toimitusmallin Internet-palveluntarjoajalle jossa tuotteiden tuki- ja ylläpito on ulkoistettu Intiaan. Teoriaosassa esitellään erilaisia toimitusmalleja ja keskitytään erityisesti hajautettuun toimitusmalliin. Tämän lisäksi luetellaan valintakriteerejä joilla voidaan arvioida projektin soveltuvuutta ulkoistettavaksi sekä esitellään mahdollisuuksia ja uhkia jotka sisältyvät globaaliin ulkoistusprosessiin. Käytäntöosassa esitellään globaali palvelun toimittamisprosessi joka on kehitetty Internet-palveluntarjoajan tarpeisiin.

Effect of radar rainfall time resolution on the predictive capability of a distributed hydrologic model

The performance of a hydrologic model depends on the rainfall input data, both spatially and temporally. As the spatial distribution of rainfall exerts a great influence on both runoff volumes and peak flows, the use of a distributed hydrologic model can improve the results in the case of convective rainfall in a basin where the storm area is smaller than the basin area. The aim of this study was to perform a sensitivity analysis of the rainfall time resolution on the results of a distributed hydrologic model in a flash-flood prone basin. Within such a catchment, floods are produced by heavy rainfall events with a large convective component. A second objective of the current paper is the proposal of a methodology that improves the radar rainfall estimation at a higher spatial and temporal resolution. Composite radar data from a network of three C-band radars with 6-min temporal and 2 × 2 km2 spatial resolution were used to feed the RIBS distributed hydrological model. A modification of the Window Probability Matching Method (gauge-adjustment method) was applied to four cases of heavy rainfall to improve the observed rainfall sub-estimation by computing new Z/R relationships for both convective and stratiform reflectivities. An advection correction technique based on the cross-correlation between two consecutive images was introduced to obtain several time resolutions from 1 min to 30 min. The RIBS hydrologic model was calibrated using a probabilistic approach based on a multiobjective methodology for each time resolution. A sensitivity analysis of rainfall time resolution was conducted to find the resolution that best represents the hydrological basin behaviour.

Calibration of structure in a distributed forecasting model for a semiarid flash flood: dynamic surface storage and channel roughness

Flash floods pose a significant danger for life and property. Unfortunately, in arid and semiarid environment the runoff generation shows a complex non-linear behavior with a strong spatial and temporal non-uniformity. As a result, the predictions made by physically-based simulations in semiarid areas are subject to great uncertainty, and a failure in the predictive behavior of existing models is common. Thus better descriptions of physical processes at the watershed scale need to be incorporated into the hydrological model structures. For example, terrain relief has been systematically considered static in flood modelling at the watershed scale. Here, we show that the integrated effect of small distributed relief variations originated through concurrent hydrological processes within a storm event was significant on the watershed scale hydrograph. We model these observations by introducing dynamic formulations of two relief-related parameters at diverse scales: maximum depression storage, and roughness coefficient in channels. In the final (a posteriori) model structure these parameters are allowed to be both time-constant or time-varying. The case under study is a convective storm in a semiarid Mediterranean watershed with ephemeral channels and high agricultural pressures (the Rambla del Albujón watershed; 556 km 2 ), which showed a complex multi-peak response. First, to obtain quasi-sensible simulations in the (a priori) model with time-constant relief-related parameters, a spatially distributed parameterization was strictly required. Second, a generalized likelihood uncertainty estimation (GLUE) inference applied to the improved model structure, and conditioned to observed nested hydrographs, showed that accounting for dynamic relief-related parameters led to improved simulations. The discussion is finally broadened by considering the use of the calibrated model both to analyze the sensitivity of the watershed to storm motion and to attempt the flood forecasting of a stratiform event with highly different behavior.

Assessing the sensitivities of a distributed snow model to forcing data resolution

Highly heterogeneous mountain snow distributions strongly affect soil moisture patterns; local ecology; and, ultimately, the timing, magnitude, and chemistry of stream runoff. Capturing these vital heterogeneities in a physically based distributed snow model requires appropriately scaled model structures. This work looks at how model scale—particularly the resolutions at which the forcing processes are represented—affects simulated snow distributions and melt. The research area is in the Reynolds Creek Experimental Watershed in southwestern Idaho. In this region, where there is a negative correlation between snow accumulation and melt rates, overall scale degradation pushed simulated melt to earlier in the season. The processes mainly responsible for snow distribution heterogeneity in this region—wind speed, wind-affected snow accumulations, thermal radiation, and solar radiation—were also independently rescaled to test process-specific spatiotemporal sensitivities. It was found that in order to accurately simulate snowmelt in this catchment, the snow cover needed to be resolved to 100 m. Wind and wind-affected precipitation—the primary influence on snow distribution—required similar resolution. Thermal radiation scaled with the vegetation structure (~100 m), while solar radiation was adequately modeled with 100–250-m resolution. Spatiotemporal sensitivities to model scale were found that allowed for further reductions in computational costs through the winter months with limited losses in accuracy. It was also shown that these modeling-based scale breaks could be associated with physiographic and vegetation structures to aid a priori modeling decisions.

Air Pollution and Mortality in Latin America The Role of Education

Background: People with less education in Europe, Asia, and the United States are at higher risk of mortality associated with daily and longer-term air pollution exposure. We examined whether educational level modified associations between mortality and ambient particulate pollution (PM(10)) in Latin America, using several timescales. Methods: The study population included people who died during 1998-2002 in Mexico City, Mexico; Santiago, Chile; and Sao Paulo, Brazil. We fit city-specific robust Poisson regressions to daily deaths for nonexternal-cause mortality, and then stratified by age, sex, and educational attainment among adults older than age 21 years (none, some primary, some secondary, and high school degree or more). Predictor variables included a natural spline for temporal trend, linear PM(10) and apparent temperature at matching lags, and day-of-week indicators. We evaluated PM(10) for lags 0 and I day, and fit an unconstrained distributed lag model for cumulative 6-day effects. Results: The effects of a 10-mu g/m(3) increment in lag 1 PM(10) on all nonextemal-cause adult mortality were for Mexico City 0.39% (95% confidence interval = 0.131/-0.65%); Sao Paulo 1.04% (0.71%-1.38%); and for Santiago 0.61% (0.40%-0.83%. We found cumulative 6-day effects for adult mortality in Santiago (0.86% [0.48%-1.23%]) and Sao Paulo (1.38% [0.85%-1.91%]), but no consistent gradients by educational status. Conclusions: PM(10) had important short- and intermediate-term effects on mortality in these Latin American cities, but associations did not differ consistently by educational level.

Effects of quantitative easing on the USA economy: A test for policy effectiveness

The catastrophic disruption in the USA financial system in the wake of the financial crisis prompted the Federal Reserve to launch a Quantitative Easing (QE) programme in late 2008. In line with Pesaran and Smith (2014), I use a policy effectiveness test to assess whether this massive asset purchase programme was effective in stimulating the economic activity in the USA. Specifically, I employ an Autoregressive Distributed Lag Model (ARDL), in order to obtain a counterfactual for the USA real GDP growth rate. Using data from 1983Q1 to 2009Q4, the results show that the beneficial effects of QE appear to be weak and rather short-lived. The null hypothesis of policy ineffectiveness is not rejected, which suggests that QE did not have a meaningful impact on output growth.

Osakkeiden hintojen muutokset makrotalouden muuttujia ennakoivina tekijöinä 2001-2011: BRIC-maat , euroalue, Japani ja USA.

The aim of this thesis is to examine stock returns as predictive indicators to macroeconomic variables in BRIC-countries, Japan, USA and euro area. We picked to represent macroeconomic variables interest rate, inflation, currency, gross domestic product and industrial production. For the beginning we examined previous studies and theory about the subject. Hypothesis of this thesis were derived from the previous studies. To conduct the results we used tests such augmented Dickey-Fuller, Engle-Granger co-integration, Granger causality and lagged distribution model. According to results stock returns do predictive macroeconomic variables and specifically changes of GDP and industrial production. There were few evidences of stock returns predictive power of inflation.

Markovian Processes, Two-Sided Autoregressions and Finite-Sample Inference for Stationary and Nonstationary Autoregressive Processes.

In this paper, we develop finite-sample inference procedures for stationary and nonstationary autoregressive (AR) models. The method is based on special properties of Markov processes and a split-sample technique. The results on Markovian processes (intercalary independence and truncation) only require the existence of conditional densities. They are proved for possibly nonstationary and/or non-Gaussian multivariate Markov processes. In the context of a linear regression model with AR(1) errors, we show how these results can be used to simplify the distributional properties of the model by conditioning a subset of the data on the remaining observations. This transformation leads to a new model which has the form of a two-sided autoregression to which standard classical linear regression inference techniques can be applied. We show how to derive tests and confidence sets for the mean and/or autoregressive parameters of the model. We also develop a test on the order of an autoregression. We show that a combination of subsample-based inferences can improve the performance of the procedure. An application to U.S. domestic investment data illustrates the method.