Results 11  20
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300
Local Maximum Likelihood Estimation and Inference
 J. Royal Statist. Soc. B
, 1998
"... Local maximum likelihood estimation is a nonparametric counterpart of the widelyused parametric maximum likelihood technique. It extends the scope of the parametric maximum likelihood method to a much wider class of parametric spaces. Associated with this nonparametric estimation scheme is the issu ..."
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Cited by 31 (4 self)
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Local maximum likelihood estimation is a nonparametric counterpart of the widelyused parametric maximum likelihood technique. It extends the scope of the parametric maximum likelihood method to a much wider class of parametric spaces. Associated with this nonparametric estimation scheme is the issue of bandwidth selection and bias and variance assessment. This article provides a unified approach to selecting a bandwidth and constructing con dence intervals in local maximum likelihood estimation. The approach is then applied to leastsquares nonparametric regression and to nonparametric logistic regression. Our experiences in these two settings show that the general idea outlined here is powerful and encouraging.
Accident prediction models with and without trend: Application of the Generalized Estimating Equations (GEE) procedure
 Transportation Research Record
"... Accident prediction models (APMs) are very useful tools for estimating the expected number of accidents on entities such as intersections and road sections. These estimates are typically used in the identification of sites for possible safety treatment and in the evaluation of such treatments. An AP ..."
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Cited by 29 (20 self)
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Accident prediction models (APMs) are very useful tools for estimating the expected number of accidents on entities such as intersections and road sections. These estimates are typically used in the identification of sites for possible safety treatment and in the evaluation of such treatments. An APM is, in essence, a mathematical equation that expresses the average accident frequency of a site as a function of traffic flow and other site characteristics. The reliability of an APM estimate is enhanced if the APM is based on data for as many years as possible especially if data for those same years are used in the safety analysis of a site. With many years of data, however, it is necessary to account for the yeartoyear variation, or trend, in accident counts because of the influence of factors that change every year. To capture this variation, the count for each year is treated as a separate observation. Unfortunately, the disaggregation of the data in this manner creates a temporal correlation that presents difficulties for traditional model calibration procedures. The objective of this paper is to present an application of a generalized estimating equations (GEE) procedure to develop an APM that incorporates trend in accident data. Data for the application pertains to a sample of 4–legged signalized intersections in Toronto, Canada for the years 1990 to 1995. The GEE model incorporating the time trend is shown to be superior to models that do not accommodate trend and/or the temporal correlation in accident data. Key words: accident prediction models, temporal correlation, GEE, GLM, signalized intersections
TapPrints: Your Finger Taps Have Fingerprints
"... This paper shows that the location of screen taps on modern smartphones and tablets can be identified from accelerometer and gyroscope readings. Our findings have serious implications, as we demonstrate that an attacker can launch a background process on commodity smartphones and tablets, and silent ..."
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Cited by 28 (0 self)
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This paper shows that the location of screen taps on modern smartphones and tablets can be identified from accelerometer and gyroscope readings. Our findings have serious implications, as we demonstrate that an attacker can launch a background process on commodity smartphones and tablets, and silently monitor the user’s inputs, such as keyboard presses and icon taps. While precise tap detection is nontrivial, requiring machine learning algorithms to identify fingerprints of closely spaced keys, sensitive sensors on modern devices aid the process. We present TapPrints, a framework for inferring the location of taps on mobile device touchscreens using motion sensor data combined with machine learning analysis. By running tests on two different offtheshelf smartphones and a tablet computer we show that identifying tap locations on the screen and inferring English letters could be done with up to 90 % and 80 % accuracy, respectively. By optimizing the core tap detection capability with additional information, such as contextual priors, we are able to further magnify the core threat.
Large Sample Theory for Semiparametric Regression Models with TwoPhase, Outcome Dependent Sampling
, 2000
"... Outcomedependent, twophase sampling designs can dramatically reduce the costs of observational studies by judicious selection of the most informative subjects for purposes of detailed covariate measurement. Here we derive asymptotic information bounds and the form of the efficient score and inuenc ..."
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Cited by 27 (9 self)
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Outcomedependent, twophase sampling designs can dramatically reduce the costs of observational studies by judicious selection of the most informative subjects for purposes of detailed covariate measurement. Here we derive asymptotic information bounds and the form of the efficient score and inuence functions for the semiparametric regression models studied by Lawless, Kalbfleisch, and Wild (1999) under twophase sampling designs. We relate the efficient score to the leastfavorable parametric submodel by use of formal calculations suggested by Newey (1994). We then proceed to show that the maximum likelihood estimators proposed by Lawless, Kalbfleisch, and Wild (1999) for both the parametric and nonparametric parts of the model are asymptotically normal and efficient, and that the efficient influence function for the parametric part agrees with the more general calculations of Robins, Hsieh, and Newey (1995).
State Evolution for General Approximate Message Passing Algorithms, with Applications to Spatial Coupling
, 2012
"... We consider a class of approximated message passing (AMP) algorithms and characterize their highdimensional behavior in terms of a suitable state evolution recursion. Our proof applies to Gaussian matrices with independent but not necessarily identically distributed entries. It covers – in particul ..."
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Cited by 24 (6 self)
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We consider a class of approximated message passing (AMP) algorithms and characterize their highdimensional behavior in terms of a suitable state evolution recursion. Our proof applies to Gaussian matrices with independent but not necessarily identically distributed entries. It covers – in particular – the analysis of generalized AMP, introduced by Rangan, and of AMP reconstruction in compressed sensing with spatially coupled sensing matrices. The proof technique builds on the one of [BM11], while simplifying and generalizing several steps. 1
Randomization Inference with Natural Experiments: An Analysis of Ballot Effects
 in the 2003 California Recall Election.” Journal of the American Statistical Association 101:888–900
, 2006
"... Since the 2000 U.S. Presidential election, social scientists have rediscovered a long tradition of research that investigates the effects of ballot format on voting. Using a new dataset collected by the New York Times, we investigate the causal effect of being listed on the first ballot page in the ..."
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Cited by 23 (4 self)
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Since the 2000 U.S. Presidential election, social scientists have rediscovered a long tradition of research that investigates the effects of ballot format on voting. Using a new dataset collected by the New York Times, we investigate the causal effect of being listed on the first ballot page in the 2003 California gubernatorial recall election. California law mandates a unique randomization procedure of ballot order that, when appropriately modeled, can be used to approximate a classical randomized experiment in a real world setting. We apply (nonparametric) randomization inference based on Fisher’s exact test, which directly incorporates the actual randomization procedure and yields accurate confidence intervals. Our results suggest that over forty percent of the minor candidates gained more votes when listed on the first page of the ballot, while there is no significant effect for top two candidates. We also investigate how randomization inference differs from conventional estimators that do not fully incorporate California’s complex treatment assignment mechanism. The results indicate appreciable differences between the two approaches.
Using Calibration Weighting to Adjust for Nonresponse and Coverage Errors, Survey Methodology 32
, 2006
"... Calibration forces the weighted estimates of certain variables to match known or alternatively estimated population totals called benchmarks. It can be used to correct for samplesurvey nonresponse or for coverage error resulting from frame undercoverage or unit duplication. The quasirandomization ..."
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Cited by 23 (9 self)
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Calibration forces the weighted estimates of certain variables to match known or alternatively estimated population totals called benchmarks. It can be used to correct for samplesurvey nonresponse or for coverage error resulting from frame undercoverage or unit duplication. The quasirandomization theory supporting its use in nonresponse adjustment treats response as an additional phase of random sampling. The functional form of a quasirandom response model is assumed to be known, its parameter values estimated implicitly through the creation of calibration weights. Unfortunately, calibration depends upon known benchmark totals 1 while the variables in a plausible model for survey response are not necessarily the same as the benchmark variables. Moreover, it may be prudent to keep the number of explanatory variables in a response model small. We will address using calibration to adjust for nonresponse when the explanatory model variables and benchmark variables are allowed to differ as long as the number of benchmark variables is at least as great as the number of model variables. Data from National Agricultural Statistical Service’s 2002 Census of Agriculture and simulations based upon that data will be used to illustrate alternative adjustments for nonresponse. The paper concludes with some remarks about extension of the methodology to adjustment for coverage error.
Trialtotrial variability and its effect on timevarying dependence between two neurons
 J. Neurophysiology
, 2005
"... The joint peristimulus time histogram (JPSTH) and crosscorrelogram provide a visual representation of correlated activity for a pair of neurons, and the way this activity may increase or decrease over time. In a companion paper (Cai et al. 2004a) we showed how a Bootstrap evaluation of the peaks in ..."
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Cited by 22 (8 self)
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The joint peristimulus time histogram (JPSTH) and crosscorrelogram provide a visual representation of correlated activity for a pair of neurons, and the way this activity may increase or decrease over time. In a companion paper (Cai et al. 2004a) we showed how a Bootstrap evaluation of the peaks in the smoothed diagonals of the JPSTH may be used to establish the likely validity of apparent timevarying correlation. As noted by Brody (1999a,b) and BenShaul et al. (2001), trialtotrial variation can confound correlation and synchrony effects. In this paper we elaborate on that observation, and present a method of estimating the timedependent trialtotrial variation in spike trains that may exceed the natural variation displayed by Poisson and nonPoisson point processes. The statistical problem is somewhat subtle because relatively few spikes per trial are available for estimating a firingrate function that fluctuates over time. The method developed here uses principal components of the trialtotrial variability in firing rate functions to obtain a small number of parameters (typically two or three) that characterize the deviation of each trial’s firing rate function from the acrosstrial average firing rate, represented by the
The geography of recent genetic ancestry across Europe. PLoS Biol 11: e1001555
, 2013
"... The recent genealogical history of human populations is a complex mosaic formed by individual migration, largescale population movements, and other demographic events. Population genomics datasets can provide a window into this recent history, as rare traces of recent shared genetic ancestry are de ..."
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Cited by 22 (1 self)
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The recent genealogical history of human populations is a complex mosaic formed by individual migration, largescale population movements, and other demographic events. Population genomics datasets can provide a window into this recent history, as rare traces of recent shared genetic ancestry are detectable due to long segments of shared genomic material. We make use of genomic data for 2257 Europeans (in the POPRES dataset) to conduct one of the first surveys of recent genealogical ancestry over the past three thousand years at a continental scale. We detected 1.9 million shared long genomic segments, and used the lengths of these to infer the distribution of shared ancestors across time and geography. We find that a pair of modern Europeans living in neighboring populations share around 2–12 genetic common ancestors from the last 1500 years, and upwards of 100 genetic ancestors from the previous 1000 years. These numbers drop off exponentially with geographic distance, but since these genetic ancestors are a tiny fraction of common genealogical ancestors, individuals from opposite ends of Europe are still expected to share millions of common genealogical ancestors over the last 1000 years. There is also substantial regional variation in the number of shared genetic ancestors. For example, there are especially high numbers of common ancestors shared between many eastern populations
2007): “Exports and productivity growth  First evidence from a continuous treatment approach
 ZEW Discussion Paper
"... www.jenecon.de ..."