Graph-Based Change-Point Detection

We consider the testing and estimation of change-points -- locations where the distribution abruptly changes -- in a data sequence. A new approach, based on scan statistics utilizing graphs representing the similarity between observations, is proposed. The graph-based approach is non-parametric, and can be applied to any data set as long as an informative similarity measure on the sample space can be defined. Accurate analytic approximations to the significance of graph-based scan statistics for both the single change-point and the changed interval alternatives are provided. Simulations reveal that the new approach has better power than existing approaches when the dimension of the data is moderate to high. The new approach is illustrated on two applications: The determination of authorship of a classic novel, and the detection of change in a network over time

Change-point model on nonhomogeneous Poisson processes with application in copy number profiling by next-generation DNA sequencing

We propose a flexible change-point model for inhomogeneous Poisson Processes, which arise naturally from next-generation DNA sequencing, and derive score and generalized likelihood statistics for shifts in intensity functions. We construct a modified Bayesian information criterion (mBIC) to guide model selection, and point-wise approximate Bayesian confidence intervals for assessing the confidence in the segmentation. The model is applied to DNA Copy Number profiling with sequencing data and evaluated on simulated spike-in and real data sets.Comment: Published in at http://dx.doi.org/10.1214/11-AOAS517 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

Harnessing the Forces of Urban Expansion - The Public Economics of Farmland Development Allowance

For decades, rapid urban expansion has led to concerns over the loss of cultivated land in rural China. This contrasts sharply with another salient feature of the Chinese land policy reform landscape that has gone on largely unnoticed - the addition of newly cultivated land in China through land development has consistently exceeded land conversion. In a model featuring fiscal decentralization, local governments as custodians of land use and development, along with a land development allowance policy instituted in 1998, we show that a land development allowance policy can harness the forces of urban expansion to encourage agricultural land development.land development allowance, fiscal decentralization, inter-jurisdictional competition, agricultural development, Community/Rural/Urban Development, H11, H77, P35, R5, R14, O18,

Detecting simultaneous variant intervals in aligned sequences

Given a set of aligned sequences of independent noisy observations, we are concerned with detecting intervals where the mean values of the observations change simultaneously in a subset of the sequences. The intervals of changed means are typically short relative to the length of the sequences, the subset where the change occurs, the "carriers," can be relatively small, and the sizes of the changes can vary from one sequence to another. This problem is motivated by the scientific problem of detecting inherited copy number variants in aligned DNA samples. We suggest a statistic based on the assumption that for any given interval of changed means there is a given fraction of samples that carry the change. We derive an analytic approximation for the false positive error probability of a scan, which is shown by simulations to be reasonably accurate. We show that the new method usually improves on methods that analyze a single sample at a time and on our earlier multi-sample method, which is most efficient when the carriers form a large fraction of the set of sequences. The proposed procedure is also shown to be robust with respect to the assumed fraction of carriers of the changes.Comment: Published in at http://dx.doi.org/10.1214/10-AOAS400 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

Detecting mutations in mixed sample sequencing data using empirical Bayes

We develop statistically based methods to detect single nucleotide DNA mutations in next generation sequencing data. Sequencing generates counts of the number of times each base was observed at hundreds of thousands to billions of genome positions in each sample. Using these counts to detect mutations is challenging because mutations may have very low prevalence and sequencing error rates vary dramatically by genome position. The discreteness of sequencing data also creates a difficult multiple testing problem: current false discovery rate methods are designed for continuous data, and work poorly, if at all, on discrete data. We show that a simple randomization technique lets us use continuous false discovery rate methods on discrete data. Our approach is a useful way to estimate false discovery rates for any collection of discrete test statistics, and is hence not limited to sequencing data. We then use an empirical Bayes model to capture different sources of variation in sequencing error rates. The resulting method outperforms existing detection approaches on example data sets.Comment: Published in at http://dx.doi.org/10.1214/12-AOAS538 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

An Analysis of Risk-Taking Behavior for Public Defined Benefit Pension Plans

The current pension funding gap for public employees through state-sponsored defined benefit (DB) plans causes concern. The effect of underfunded pension plans and the consequent risk-taking touch a large percentage of the population, including those at risk and the taxpayers who may be ultimately called upon to close the funding gap The severe funding gap prompts questions of why the underfunding occurred, and how the underfunding will affect state and local budgets, public employee retirement benefits, and employment security. A pertinent question is whether pension fund administrators will adopt riskier investment strategies in the hope of raising pension fund returns and lowering shortfalls, thus minimizing state pension contributions and reducing state budget gaps. In this research project, the authors focus on risk-taking behavior of public pension plans. In particular, the first comprehensive analysis of the determinants of public pension risk-taking behavior during the period 2001 through 2009 will be provided. Researchers will also investigate whether states reduced pension investment risk after the recent financial crisis. These results will have important policy relevance regarding public employee retirement benefit and job security, tax policies, state fiscal constraints, political influence, and public employee unionization, among other issues

Public Pension Crisis and Investment Risk Taking: Underfunding, Fiscal Constraints, Public Accounting, and Policy Implications

Public pension funds that cover retirement benefits for almost 20 million active or retired employees have been significantly underfunded. An important, though largely overlooked, issue related to pension underfunding is the excessive investment risk levels assumed by public plans. Our analysis suggests government accounting standards strongly affect public fund investment risk, as higher return assumptions (used to discount pension liabilities) are associated with higher investment risk. Public funds undertake more risk if they are underfunded and have lower investment returns in previous years, consistent with the risk transfer hypothesis. Furthermore, pension funds in states facing fiscal constraints allocate more assets to equity and have higher betas. There also appears to be a herding effect in that a change in CalPERS portfolio beta or equity allocation is mimicked by other pension funds. Solutions to excessive investment risk include use of more realistic discount rates such as a Treasury rate or a municipal bond yield to estimate liabilities and regulations or practices that reduce the ability of a plan to shift an underfunding burden to future generations