Journal of the American Statistical Association

最新文章

• Approximations of the Optimal Importance Density Using Gaussian Particle Flow Importance Sampling AI翻译
  作者：Bunch, Pete; Godsill, Simon
  作者单位：University of Cambridge
  摘要：Recently developed particle flow algorithms provide an alternative to importance sampling for drawing particles from a posterior distribution, and a number of particle filters based on this principle have been proposed. Samples are drawn from the prior and then moved according to some dynamics over an interval of pseudo-time such that their final values are distributed according to the desired posterior. In practice, implementing a particle flow, sampler requires multiple layers of approximati...
• Using a Monotonic Density Ratio Model to Find the Asymptotically Optimal Combination of Multiple Diagnostic Tests AI翻译
  作者：Chen, Baojiang; Li, Pengfei; Qin, Jing; Yu, Tao
  作者单位：University of Nebraska System; University of Nebraska Medical Center
  摘要：With the advent of new technology, new biomarker studies have become essential in cancer research. To achieve optimal sensitivity and specificity, one needs to combine different diagnostic tests. The celebrated Neyman Pearson lemma enables us to use the density ratio to optimally combine different diagnostic tests. In this article, we propose a semiparametric model by directly modeling, the density ratio between the diseased and nondiseased population as an unspecified monotonic nondecreasing ...
• Detection of Infectious Disease Outbreaks From Laboratory Data With Reporting Delays AI翻译
  作者：Noufaily, Angela; Farrington, Paddy; Garthwaite, Paul; Enki, Doyo Gragn; Andrews, Nick; Charlett, Andre
  作者单位：University of Warwick
  摘要：Many statistical surveillance systems for the timely detection of outbreaks of infectious disease operate on laboratory data. Such data typically incur reporting delays between the time at which a specimen is collected for diagnostic purposes, and the time at which the results of the laboratory analysis become available. Statistical surveillance systems currently in use usually make some ad hoc adjustment for such delays, or use counts by time of report. We propose a new statistical approach t...
• Understanding the Impact of Stroke on Brain Motor Function: A Hierarchical Bayesian Approach AI翻译
  作者：Yu, Zhe; Prado, Raquel; Quinlan, Erin Burke; Cramer, Steven C.; Ombao, Hernando
  作者单位：University of California System; University of California Santa Cruz
  摘要：Stroke is a disturbance in blood supply to the brain resulting in the loss of brain functions, particularly motor function. A study was conducted by the UCI Neurorehabilitation Lab to investigate the impact of stroke on motor-related brain regions. Functional MRI (fMRI) data were collected from stroke patients and healthy controls while the subjects performed a simple motor task. In addition to affecting local neuronal activation strength, stroke might also alter communications (i.e., connecti...
• Comment AI翻译
  作者：Haneuse, Sebastien; Rivera, Claudia
  作者单位：Harvard University; Harvard T.H. Chan School of Public Health
• Perturbation Detection Through Modeling of Gene Expression on a Latent Biological Pathway Network: A Bayesian Hierarchical Approach AI翻译
  作者：Pham, Lisa M.; Carvalho, Luis; Schaus, Scott; Kolaczyk, Eric D.
  作者单位：Boston University; Boston University
  摘要：Cellular response to a perturbation is the result of a dynamic system of biological variables linked in a complex network. A major challenge in drug and disease studies is identifying the key factors of a biological network that are essential in determining the cell's fate. Here, our goal is the identification of perturbed pathways from high-throughput gene expression data. We develop a three-level hierarchical model, where (i) the first level captures the relationship between gene expression ...
• Statistical Inference in Hidden Markov Models Using k-Segment Constraints AI翻译
  作者：Titsias, Michalis K.; Holmes, Christopher C.; Yau, Christopher
  作者单位：Athens University of Economics & Business; University of Oxford; Wellcome Centre for Human Genetics; University of Oxford; UK Research & Innovation (UKRI); Medical Research Council UK (MRC)
  摘要：Hidden Markov models (HMMs) are one of the most widely used statistical methods for analyzing sequence data. However, the reporting of output from HMMs has largely been restricted to the presentation of the most-probable (MAP) hidden state sequence, found via the Viterbi algorithm, or the sequence of most probable marginals using the forward-backward algorithm. In this article, we expand the amount of information we could obtain from the posterior distribution of an HMM by introducing linear-t...
• Rejoinder AI翻译
  作者：Airoldi, Edoardo M.
  作者单位：Harvard University
• Localizing Temperature Risk AI翻译
  作者：Haerdle, Wolfgang Karl; Cabrera, Brenda Lopez; Okhrin, Ostap; Wang, Weining
  作者单位：Humboldt University of Berlin; Technische Universitat Dresden
  摘要：On the temperature derivative market, modeling temperature volatility is an important issue for pricing and hedging. To apply the pricing tools of financial mathematics, one needs to isolate a Gaussian risk factor. A conventional model for temperature dynamics is a stochastic model with seasonality and intertemporal autocorrelation. Empirical work based on seasonality and autocorrelation correction reveals that the obtained residuals are heteroscedastic with a periodic pattern. The object of t...
• Personalized Dose Finding Using Outcome Weighted Learning Comment AI翻译
  作者：Rosenblum, Michael
  作者单位：Johns Hopkins University; Johns Hopkins Bloomberg School of Public Health