Statistical inference of transmission fidelity of DNA methylation patterns over somatic cell divisions in mammals

We develop Bayesian inference methods for a recently-emerging type of epigenetic data to study the transmission fidelity of DNA methylation patterns over cell divisions. The data consist of parent-daughter double-stranded DNA methylation patterns with each pattern coming from a single cell and represented as an unordered pair of binary strings. The data are technically difficult and time-consuming to collect, putting a premium on an efficient inference method. Our aim is to estimate rates for the maintenance and de novo methylation events that gave rise to the observed patterns, while accounting for measurement error. We model data at multiple sites jointly, thus using whole-strand information, and considerably reduce confounding between parameters. We also adopt a hierarchical structure that allows for variation in rates across sites without an explosion in the effective number of parameters. Our context-specific priors capture the expected stationarity, or near-stationarity, of the stochastic process that generated the data analyzed here. This expected stationarity is shown to greatly increase the precision of the estimation. Applying our model to a data set collected at the human FMR1 locus, we find that measurement errors, generally ignored in similar studies, occur at a nontrivial rate (inappropriate bisulfite conversion error: 1.6$$ with 80$$ CI: 0.9--2.3$$). Accounting for these errors has a substantial impact on estimates of key biological parameters. The estimated average failure of maintenance rate and daughter de novo rate decline from 0.04 to 0.024 and from 0.14 to 0.07, respectively, when errors are accounted for. Our results also provide evidence that de novo events may occur on both parent and daughter strands: the median parent and daughter de novo rates are 0.08 (80$$ CI: 0.04--0.13) and 0.07 (80$$ CI: 0.04--0.11), respectively.Comment: Published in at http://dx.doi.org/10.1214/09-AOAS297 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

Robust estimation of risks from small samples

Data-driven risk analysis involves the inference of probability distributions from measured or simulated data. In the case of a highly reliable system, such as the electricity grid, the amount of relevant data is often exceedingly limited, but the impact of estimation errors may be very large. This paper presents a robust nonparametric Bayesian method to infer possible underlying distributions. The method obtains rigorous error bounds even for small samples taken from ill-behaved distributions. The approach taken has a natural interpretation in terms of the intervals between ordered observations, where allocation of probability mass across intervals is well-specified, but the location of that mass within each interval is unconstrained. This formulation gives rise to a straightforward computational resampling method: Bayesian Interval Sampling. In a comparison with common alternative approaches, it is shown to satisfy strict error bounds even for ill-behaved distributions.Comment: 13 pages, 3 figures; supplementary information provided. A revised version of this manuscript has been accepted for publication in Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Science

Bednets, Information and Malaria in Orissa

This paper studies the identification and estimation of a basic model of technology adoption using specifcally collected information on subjective beliefs and expectations to identify key model parameters. We discuss identifcation with both non-parametrically and parametrically specified utility as well as parametric and semi-parametric specifcations for unobserved heterogeneity. We propose parametric and semi-parametric estimation methods to recover underlying preferences and use the model to study the adoption of Insecticide Treated Nets (ITNs) among poor households in rural India. We carry out counterfactual exercises to examine the effects of price and belief changes on net ownership decisions. The results suggest that purchase decisions are relatively insensitive to changes from current prices and beliefs. The method proposed here should have applicability to other discrete choice settings with non-linear indices.Malaria, Expectations, Bednets, Identication, Median Restrictions

Monetary Policy Transmission, Interest Rate Rules and Inflation Targeting in Three Transition Countries

In 1991, the rate of inflation in the Czech Republic, Hungary and Poland was between 35% and 70%. At the end of 2001, it is below 8%. We setup a small structural macro model of these economies to explain the process of disinflation. Contrary to a widespread skepticism, which permeated a large part of previous research on these issues, we show that a simple open macroeconomic model, along the lines of Svensson (2000, Journal of International Economics), with forward-looking inflation and exchange rate expectations, can adequately characterize the relationship between the output gap, inflation, the real interest rate and the exchange rate during the course of transition. We use the estimated models to interpret the main features of monetary policy in each country and identify the channels of policy transmission. We characterize the policy rules and assess the relative importance of the interest rate channel (on aggregate demand) and the exchange rate channel (which affects both aggregate demand and supply) in determining the path of (dis)inflation. In the same context, we also tentatively analyze the consequences of attempting a faster path of disinflation. Finally, we evaluate the appropriateness of the inflation targeting framework which has been adopted recently in all three countries, and discuss to what extent it represents a discontinuity with the past.Disinflation policy, Interest rate rules, Inflation targeting, Transition economies, Small open-economy macro models.

The path inference filter: model-based low-latency map matching of probe vehicle data

We consider the problem of reconstructing vehicle trajectories from sparse sequences of GPS points, for which the sampling interval is between 10 seconds and 2 minutes. We introduce a new class of algorithms, called altogether path inference filter (PIF), that maps GPS data in real time, for a variety of trade-offs and scenarios, and with a high throughput. Numerous prior approaches in map-matching can be shown to be special cases of the path inference filter presented in this article. We present an efficient procedure for automatically training the filter on new data, with or without ground truth observations. The framework is evaluated on a large San Francisco taxi dataset and is shown to improve upon the current state of the art. This filter also provides insights about driving patterns of drivers. The path inference filter has been deployed at an industrial scale inside the Mobile Millennium traffic information system, and is used to map fleets of data in San Francisco, Sacramento, Stockholm and Porto.Comment: Preprint, 23 pages and 23 figure