skip to main content

Search for: All records

Creators/Authors contains: "Handcock, Mark S."

Note: When clicking on a Digital Object Identifier (DOI) number, you will be taken to an external site maintained by the publisher. Some full text articles may not yet be available without a charge during the embargo (administrative interval).
What is a DOI Number?

Some links on this page may take you to non-federal websites. Their policies may differ from this site.

  1. Abstract

    Claiming causal inferences in network settings necessitates careful consideration of the often complex dependency between outcomes for actors. Of particular importance are treatment spillover or outcome interference effects. We consider causal inference when the actors are connected via an underlying network structure. Our key contribution is a model for causality when the underlying network is endogenous; where the ties between actors and the actor covariates are statistically dependent. We develop a joint model for the relational and covariate generating process that avoids restrictive separability and fixed network assumptions, as these rarely hold in realistic social settings. While our framework can be used with general models, we develop the highly expressive class of Exponential-family Random Network models (ERNM) of which Markov random fields and Exponential-family Random Graph models are special cases. We present potential outcome-based inference within a Bayesian framework and propose a modification to the exchange algorithm to allow for sampling from ERNM posteriors. We present results of a simulation study demonstrating the validity of the approach. Finally, we demonstrate the value of the framework in a case study of smoking in the context of adolescent friendship networks.

    more » « less
  2. Abstract

    Many demographic problems require models for partnership formation. We consider a model for matchings within a bipartite population where individuals have utility for people based on observed and unobserved characteristics. It represents both the availability of potential partners of different types and the preferences of individuals for such people. We develop an estimator for the preference parameters based on sample survey data on partnerships and population composition. We conduct simulation studies based on the Survey of Income and Program Participation showing that the estimator recovers preference parameters that are invariant under different population availabilities and has the correct confidence coverage.

    more » « less
  3. Abstract. The total Antarctic sea ice extent (SIE) experiences a distinct annual cycle, peaking in September and reaching its minimum in February. In thispaper we propose a mathematical and statistical decomposition of this temporal variation inSIE. Each component is interpretable and, when combined,gives a complete picture of the variation in the sea ice. We consider timescales varying from the instantaneous and not previously defined to themulti-decadal curvilinear trend, the longest. Because our representation is daily, these timescales of variability give precise information about thetiming and rates of advance and retreat of the ice and may be used to diagnose physical contributors to variability in the sea ice. We definea number of annual cycles each capturing different components of variation, especially the yearly amplitude and phase that are major contributors toSIE variation. Using daily sea ice concentration data, we show that our proposed invariant annual cycle explains 29 % more of the variation indaily SIE than the traditional method. The proposed annual cycle that incorporates amplitude and phase variation explains 77 % more variation thanthe traditional method. The variation in phase explains more of the variability in SIE than the amplitude. Using our methodology, we show that theanomalous decay of sea ice in 2016 was associated largely with a change of phase rather than amplitude. We show that the long term trend inAntarctic sea ice extent is strongly curvilinear and the reported positive linear trend is small and dependent strongly on a positive trend thatbegan around 2011 and continued until 2016. 
    more » « less
  4. Abstract. Landfast sea ice (fast ice) is an important though poorly understood component of the cryosphere on the Antarctic continental shelf, where it plays a key role in atmosphere–ocean–ice-sheet interaction and coupled ecological and biogeochemical processes. Here, we present a first in-depth baseline analysis of variability and change in circum-Antarctic fast-ice distribution (including its relationship to bathymetry), based on a new high-resolution satellite-derived time series for the period 2000 to 2018. This reveals (a) an overall trend of -882±824 km2 yr−1 (-0.19±0.18 % yr−1) and (b) eight distinct regions in terms of fast-ice coverage and modes of formation. Of these, four exhibit positive trends over the 18-year period and four negative. Positive trends are seen in East Antarctica and in the Bellingshausen Sea, with this region claiming the largest positive trend of +1198±359 km2 yr−1 (+1.10±0.35 % yr−1). The four negative trends predominantly occur in West Antarctica, with the largest negative trend of -1206±277 km2 yr−1 (-1.78±0.41 % yr−1) occurring in the Victoria and Oates Land region in the western Ross Sea. All trends are significant. This new baseline analysis represents a significant advance in our knowledge of the current state of both the global cryosphere and the complex Antarctic coastal system, which are vulnerable to climate variability and change. It will also inform a wide range of other studies. 
    more » « less
  5. Abstract

    Understanding the variability of Antarctic sea ice is an ongoing challenge given the limitations of observed data. Coupled climate model simulations present the opportunity to examine this variability in Antarctic sea ice. Here, the daily sea ice extent simulated by the newly released National Center for Atmospheric Research (NCAR) Community Eart h System Model Version 2 (CESM2) for the historical period (1979–2014) is compared to the satellite‐observed daily sea ice extent for the same period. The comparisons are made using a newly developed suite of statistical metrics that estimates the variability of the sea ice extent on timescales ranging from the long‐term decadal to the short term, intraday scales. Assessed are the annual cycle, trend, day‐to‐day change, and the volatility, a new statistic that estimates the variability at the daily scale. Results show that the trend in observed daily sea ice is dominated by subdecadal variability with a weak positive linear trend superimposed. The CESM2 simulates comparable subdecadal variability but with a strong negative linear trend superimposed. The CESM2's annual cycle is similar in amplitude to the observed, key differences being the timing of ice advance and retreat. The sea ice begins its advance later, reaches its maximum later and begins retreat later in the CESM2. This is confirmed by the day‐to‐day change. Apparent in all of the sea ice regions, this behavior suggests the influence of the semiannual oscillation of the circumpolar trough. The volatility, which is associated with smaller scale dynamics such as storms, is smaller in the CESM2 than observed.

    more » « less
  6. Abstract

    Respondent-driven sampling (RDS) is commonly used to study hard-to-reach populations since traditional methods are unable to efficiently survey members due to the typically highly stigmatized nature of the population. The number of people in these populations is of primary global health and demographic interest and is usually hard to estimate. However, due to the nature of RDS, current methods of population size estimation are insufficient. We introduce a new method of estimating population size that uses concepts from capture-recapture methods while modeling RDS as a successive sampling process. We assess its statistical validity using information from the CDC’s National HIV Behavioral Surveillance system in 2009 and 2012.

    more » « less
  7. Abstract

    We develop a Bayesian model–based approach to finite population estimation accounting for spatial dependence. Our innovation here is a framework that achieves inference for finite population quantities in spatial process settings. A key distinction from the small area estimation setting is that we analyze finite populations referenced by their geographic coordinates. Specifically, we consider a two‐stage sampling design in which the primary units are geographic regions, the secondary units are point‐referenced locations, and the measured values are assumed to be a partial realization of a spatial process. Estimation of finite population quantities from geostatistical models does not account for sampling designs, which can impair inferential performance, whereas design‐based estimates ignore the spatial dependence in the finite population. We demonstrate by using simulation experiments that process‐based finite population sampling models improve model fit and inference over models that fail to account for spatial correlation. Furthermore, the process‐based models offer richer inference with spatially interpolated maps over the entire region. We reinforce these improvements and demonstrate scalable inference for groundwater nitrate levels in the population of California Central Valley wells by offering estimates of mean nitrate levels and their spatially interpolated maps.

    more » « less