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1. Synaptic counts approximate synaptic contact area in Drosophila.

Author

Christopher L Barnes, Daniel Bonnéry, and Albert Cardona

Subjects
Medicine, Science
Abstract

The pattern of synaptic connections among neurons defines the circuit structure, which constrains the computations that a circuit can perform. The strength of synaptic connections is costly to measure yet important for accurate circuit modeling. Synaptic surface area has been shown to correlate with synaptic strength, yet in the emerging field of connectomics, most studies rely instead on the counts of synaptic contacts between two neurons. Here we quantified the relationship between synaptic count and synaptic area as measured from volume electron microscopy of the larval Drosophila central nervous system. We found that the total synaptic surface area, summed across all synaptic contacts from one presynaptic neuron to a postsynaptic one, can be accurately predicted solely from the number of synaptic contacts, for a variety of neurotransmitters. Our findings support the use of synaptic counts for approximating synaptic strength when modeling neural circuits.

Published
2022
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2. Synaptic counts approximate synaptic contact area in Drosophila

Author

Christopher L. Barnes, Daniel Bonnéry, Albert Cardona, Barnes, Christopher L [0000-0002-1296-7310], Bonnéry, Daniel [0000-0001-8582-7856], Cardona, Albert [0000-0003-4941-6536], and Apollo - University of Cambridge Repository

Subjects
Neurons, Medicine and health sciences, Connectomics, Neurotransmitter Agents, Multidisciplinary, Biology and life sciences, Biology, Circuit modeling, Presynaptic neuron, Synaptic contact, Research and analysis methods, Postsynaptic potential, Larva, Synapses, Biological neural network, Connectome, Animals, Drosophila, Neuroscience, Research Article
Abstract

Funder: Howard Hughes Medical Institute; funder-id: http://dx.doi.org/10.13039/100000011, The pattern of synaptic connections among neurons defines the circuit structure, which constrains the computations that a circuit can perform. The strength of synaptic connections is costly to measure yet important for accurate circuit modeling. Synaptic surface area has been shown to correlate with synaptic strength, yet in the emerging field of connectomics, most studies rely instead on the counts of synaptic contacts between two neurons. Here we quantified the relationship between synaptic count and synaptic area as measured from volume electron microscopy of the larval Drosophila central nervous system. We found that the total synaptic surface area, summed across all synaptic contacts from one presynaptic neuron to a postsynaptic one, can be accurately predicted solely from the number of synaptic contacts, for a variety of neurotransmitters. Our findings support the use of synaptic counts for approximating synaptic strength when modeling neural circuits.

Published
2022

3. The Promise and Limitations of Synthetic Data as a Strategy to Expand Access to State-Level Multi-Agency Longitudinal Data

Author

Angela K. Henneberger, Mark Lachowicz, Terry Shaw, Laura M. Stapleton, Michael E. Woolley, Tessa L. Johnson, Yi Feng, Daniel Bonnéry, Bess A. Rose, and Yating Zheng

Subjects
Information management, Program evaluation, Data collection, Computer science, Cost effectiveness, 05 social sciences, Information Dissemination, 050301 education, Information security, Policy analysis, Synthetic data, Education, Risk analysis (engineering), 0502 economics and business, 050207 economics, 0503 education
Abstract

There is demand among policy-makers for the use of state education longitudinal data systems, yet laws and policies regulating data disclosure limit access to such data, and security concerns and r...

Published
2019
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4. An Evaluation of Design-based Properties of Different Composite Estimators

Author

Partha Lahiri, Daniel Bonnéry, and Yang Cheng

Subjects
FOS: Computer and information sciences, Statistics and Probability, ddc:519, longitudinal survey, Mean squared error, Calibration (statistics), Statistics & Probability, labor force statistics, Estimator, Variance (accounting), calibration, estimated controls, Regression, Methodology (stat.ME), Sampling design, Statistics, 62D05, Statistics, Probability and Uncertainty, Linear combination, lcsh:Statistics, lcsh:HA1-4737, Rotation (mathematics), Statistics - Methodology, Mathematics
Abstract

For the last several decades, the US Census Bureau has been applying AK composite estimation method for estimating monthly levels and month-to-month changes in unemployment using data from the Current Population Survey (CPS), which uses a rotating panel design. For each rotation group, survey-weighted totals, known as month-in-sample estimates, are derived each month to estimate population totals. Denoting the vector of month-in-sample estimates by Y and the design-based variance-covariance matrix of Y by ∑, one can obtain a class of AK estimators as linear combinations of Y, where the coefficients of a linear combination in the class are functions of the two coefficients A and K. The coefficients A and K were optimized by the Census Bureau under rather strong assumptions on ∑ such as the stationarity of ∑ over a decade. We devise an evaluation study in order to compare the AK estimator with a number of rival estimators. To this end, we construct three different synthetic populations that resemble the Current Population Survey (CPS) data. To draw samples from these synthetic populations, we consider a simplified sample design that mimics the CPS sample design with the same rotation pattern. Since the number of possible samples that can be drawn from each synthetic population is not large, we compute the exact ∑ and the exact mean squared error of all estimators considered to facilitate comparison. To generate the first set of rival estimators, we consider certain subclasses of the broader class of linear combinations of month-in-sample estimates. For each subclass, when ∑ is known, the optimum estimator is obtained as a function of ∑. An estimated optimal estimator in each subclass is then obtained from the corresponding optimal estimator when ∑ is replaced by an estimator. Neither the AK estimator nor the estimated optimal estimators for these subclasses performed well in our evaluation study. In our real life data analysis, the AK estimates are constantly below the survey-weighted estimates, indicating potential bias. Our study indicates limitations of the approach that generate an estimated optimal estimator by first obtaining the optimal estimator in a class of linear combination of Y and then substituting in the optimal estimator an estimate of ∑. Any attempt to improve on the estimated optimal estimator in any given class would require a thorough investigation of the highly non-trivial problem of estimation of ∑ for a complex setting like the CPS. We have not discussed this problem in this paper. Instead, we adapted the regression composite estimator used by Statistics Canada in the CPS setting. Unlike the estimated optimal estimators, the regression composite estimator does not require estimation of ∑ and is less sensitive to the rotation group bias in our simulation study. Our study indicates that there is a great potential for regression composite estimation technique in improving estimation of both levels and month-to-month changes in the unemployment rates.

Published
2018

5. Asymptotics for the maximum sample likelihood estimator under informative selection from a finite population

Author

Daniel Bonnéry, François Coquet, F. Jay Breidt, Centre de Recherche en Économie et Statistique (CREST), Ecole Nationale de la Statistique et de l'Analyse de l'Information [Bruz] (ENSAI)-École polytechnique (X)-École Nationale de la Statistique et de l'Administration Économique (ENSAE Paris)-Centre National de la Recherche Scientifique (CNRS), Department of Statistics, Colorado State University [Fort Collins] (CSU), Institut de Recherche Mathématique de Rennes (IRMAR), AGROCAMPUS OUEST, Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Université de Rennes 2 (UR2), Université de Rennes (UNIV-RENNES)-École normale supérieure - Rennes (ENS Rennes)-Centre National de la Recherche Scientifique (CNRS)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA), Centre de Recherche en Économie et Statistique ( CREST ), Ecole Nationale de la Statistique et de l'Analyse de l'Information [Bruz] ( ENSAI ) -École polytechnique ( X ) -École Nationale de la Statistique et de l'Administration Économique ( ENSAE ParisTech ), Colorado State University [Fort Collins] ( CSU ), Institut de Recherche Mathématique de Rennes ( IRMAR ), Université de Rennes 1 ( UR1 ), Université de Rennes ( UNIV-RENNES ) -Université de Rennes ( UNIV-RENNES ) -AGROCAMPUS OUEST-École normale supérieure - Rennes ( ENS Rennes ) -Institut National de Recherche en Informatique et en Automatique ( Inria ) -Institut National des Sciences Appliquées ( INSA ) -Université de Rennes 2 ( UR2 ), Université de Rennes ( UNIV-RENNES ) -Centre National de la Recherche Scientifique ( CNRS ), Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-École normale supérieure - Rennes (ENS Rennes)-Université de Rennes 2 (UR2)-Centre National de la Recherche Scientifique (CNRS)-INSTITUT AGRO Agrocampus Ouest, and Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)

Subjects
Statistics and Probability, Complex survey, weighted distribution, Estimation theory, Restricted maximum likelihood, 05 social sciences, 050401 social sciences methods, Estimator, Asymptotic distribution, Conditional probability distribution, stratified sampling, 16. Peace & justice, 01 natural sciences, Likelihood principle, Pseudo-likelihood, 010104 statistics & probability, 0504 sociology, [MATH.MATH-ST]Mathematics [math]/Statistics [math.ST], Statistics, [ MATH.MATH-ST ] Mathematics [math]/Statistics [math.ST], 0101 mathematics, Likelihood function, Selection (genetic algorithm), Mathematics
Abstract

International audience; Inference for the parametric distribution of a response given covariates is considered under informative selection of a sample from a finite population. Under this selection, the conditional distribution of a response in the sample, given the covariates and given that it was selected for observation, is not the same as the conditional distribution of the response in the finite population, given only the covariates. It is instead a weighted version of the conditional distribution of interest. Inference must be modified to account for this informative selection. An established approach in this context is maximum “sample likelihood”, developing a weight function that reflects the informative sampling design, then treating the observations as if they were independently distributed according to the weighted distribution. While the sample likelihood methodology has been widely applied, its theoretical foundation has been less developed. A precise asymptotic description of a wide range of informative selection mechanisms is proposed. Under this framework, consistency and asymptotic normality of the maximum sample likelihood estimators are established. The theory allows for the possibility of nuisance parameters that describe the selection mechanism. The proposed regularity conditions are verifiable for various sample schemes, motivated by real problems in surveys. Simulation results for these examples illustrate the quality of the asymptotic approximations, and demonstrate a practical approach to variance estimation that combines aspects of model-based information theory and design-based variance estimation.

Published
2018
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6. Triple-goal estimation of unemployment rates for U.S. states using the U.S. Current Population Survey data

Author

Daniel Bonnéry, Yang Cheng, Partha Lahiri, and Neung Soo Ha

Subjects
Statistics and Probability, Estimation, ddc:519, small area estimation, Computer science, media_common.quotation_subject, Monte Carlo method, Markov chain Monte Carlo, Variance (accounting), Empirical distribution function, Monte Carlo Markov Chain, empirical distribution function, symbols.namesake, Small area estimation, rank, Ranking, Unemployment, Statistics, Econometrics, symbols, complex survey data, Statistics, Probability and Uncertainty, media_common, risk
Abstract

In this paper, we first develop a triple-goal small area estimation methodology for simultaneous estimation of unemployment rates for U.S. states using the Current Population Survey (CPS) data and a two-level random sampling variance normal model. The main goal of this paper is to illustrate the utility of the triple-goal methodology in generating a single series of unemployment rate estimates for three separate purposes: developing estimates for individual small area means, producing empirical distribution function (EDF) of true small area means, and the ranking of the small areas by true small area means. We achieve our goal using a Monte Carlo simulation experiment and a real data analysis.

Published
2017

7. Kernel estimation for a superpopulation probability density function under informative selection

Author

Daniel Bonnéry, F. Jay Breidt, François Coquet, Joint Program in Survey Methodology, University of Maryland [College Park], Department of Statistics, Colorado State University [Fort Collins] ( CSU ), Institut de Recherche Mathématique de Rennes ( IRMAR ), Université de Rennes 1 ( UR1 ), Université de Rennes ( UNIV-RENNES ) -Université de Rennes ( UNIV-RENNES ) -AGROCAMPUS OUEST-École normale supérieure - Rennes ( ENS Rennes ) -Institut National de Recherche en Informatique et en Automatique ( Inria ) -Institut National des Sciences Appliquées ( INSA ) -Université de Rennes 2 ( UR2 ), Université de Rennes ( UNIV-RENNES ) -Centre National de la Recherche Scientifique ( CNRS ), University of Maryland System-University of Maryland System, Colorado State University [Fort Collins] (CSU), Institut de Recherche Mathématique de Rennes (IRMAR), AGROCAMPUS OUEST, Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Université de Rennes 2 (UR2), Université de Rennes (UNIV-RENNES)-École normale supérieure - Rennes (ENS Rennes)-Centre National de la Recherche Scientifique (CNRS)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA), Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-École normale supérieure - Rennes (ENS Rennes)-Université de Rennes 2 (UR2)-Centre National de la Recherche Scientifique (CNRS)-INSTITUT AGRO Agrocampus Ouest, and Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)

Subjects
Statistics and Probability, education.field_of_study, Bochner's lemma, 010102 general mathematics, Kernel density estimation, Population, Nonparametric statistics, Conditional probability, Probability density function, 01 natural sciences, Nadaraya-Watson estimator, 010104 statistics & probability, [MATH.MATH-ST]Mathematics [math]/Statistics [math.ST], Statistics, Range (statistics), survey weighting D, [ MATH.MATH-ST ] Mathematics [math]/Statistics [math.ST], 0101 mathematics, education, Selection (genetic algorithm), ComputingMilieux_MISCELLANEOUS, Parametric statistics, Mathematics, complex survey
Abstract

Kernel density estimation of the probability density function (pdf) of a response variable is considered under informative selection from a finite population. The informative selection implies that the conditional pdf of a response, given that it was selected for observation, is not the same as the inferential target, which is the unconditional pdf of the response in the superpopulation. Instead, the pdf of the observations (sample pdf) is a weighted version of the superpopulation pdf of interest. Properties of the standard kernel density estimator are described under an asymptotic framework that covers a wide range of informative selection mechanisms. The theory allows for the possibility that the selection mechanism has a parametric structure. A variety of adjustments (parametric or nonparametric) to account for the informative selection are proposed, and investigated via simulation.

Published
2017
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8. Uniform convergence of the empirical cumulative distribution function under informative selection from a finite population

Author

François Coquet, Daniel Bonnéry, F. Jay Breidt, Centre de Recherche en Économie et Statistique ( CREST ), Ecole Nationale de la Statistique et de l'Analyse de l'Information [Bruz] ( ENSAI ) -École polytechnique ( X ) -École Nationale de la Statistique et de l'Administration Économique ( ENSAE ParisTech ), Institut de Recherche Mathématique de Rennes ( IRMAR ), Université de Rennes 1 ( UR1 ), Université de Rennes ( UNIV-RENNES ) -Université de Rennes ( UNIV-RENNES ) -AGROCAMPUS OUEST-École normale supérieure - Rennes ( ENS Rennes ) -Institut National de Recherche en Informatique et en Automatique ( Inria ) -Institut National des Sciences Appliquées ( INSA ) -Université de Rennes 2 ( UR2 ), Université de Rennes ( UNIV-RENNES ) -Centre National de la Recherche Scientifique ( CNRS ), Centre de Recherche en Économie et Statistique (CREST), Ecole Nationale de la Statistique et de l'Analyse de l'Information [Bruz] (ENSAI)-École polytechnique (X)-École Nationale de la Statistique et de l'Administration Économique (ENSAE Paris)-Centre National de la Recherche Scientifique (CNRS), Institut de Recherche Mathématique de Rennes (IRMAR), Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-École normale supérieure - Rennes (ENS Rennes)-Université de Rennes 2 (UR2)-Centre National de la Recherche Scientifique (CNRS)-INSTITUT AGRO Agrocampus Ouest, Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro), AGROCAMPUS OUEST, Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Université de Rennes 2 (UR2), Université de Rennes (UNIV-RENNES)-École normale supérieure - Rennes (ENS Rennes)-Centre National de la Recherche Scientifique (CNRS)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), and Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)

Subjects
Statistics and Probability, Independent and identically distributed random variables, Uniform convergence, length-biased sampling, Population, Probability density function, Mathematics - Statistics Theory, Statistics Theory (math.ST), 01 natural sciences, 010104 statistics & probability, 0502 economics and business, FOS: Mathematics, Applied mathematics, Glivenko–Cantelli, Almost surely, 0101 mathematics, education, Selection (genetic algorithm), superpopulation, 050205 econometrics, Mathematics, complex survey, education.field_of_study, 05 social sciences, cut-off sampling, endogenous stratification, [STAT.TH]Statistics [stat]/Statistics Theory [stat.TH], Empirical distribution function, [ STAT.TH ] Statistics [stat]/Statistics Theory [stat.TH], Glivenko-Cantelli, Random variable
Abstract

Consider informative selection of a sample from a finite population. Responses are realized as independent and identically distributed (i.i.d.) random variables with a probability density function (p.d.f.) f, referred to as the superpopulation model. The selection is informative in the sense that the sample responses, given that they were selected, are not i.i.d. f. In general, the informative selection mechanism may induce dependence among the selected observations. The impact of such dependence on the empirical cumulative distribution function (c.d.f.) is studied. An asymptotic framework and weak conditions on the informative selection mechanism are developed under which the (unweighted) empirical c.d.f. converges uniformly, in $L_2$ and almost surely, to a weighted version of the superpopulation c.d.f. This yields an analogue of the Glivenko-Cantelli theorem. A series of examples, motivated by real problems in surveys and other observational studies, shows that the conditions are verifiable for specified designs., Published in at http://dx.doi.org/10.3150/11-BEJ369 the Bernoulli (http://isi.cbs.nl/bernoulli/) by the International Statistical Institute/Bernoulli Society (http://isi.cbs.nl/BS/bshome.htm)

Published
2012
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9. Optimal inclusion probabilities for balanced sampling

Author

Jean-Claude Deville, Daniel Bonnéry, Guillaume Chauvet, Centre de Recherche en Économie et Statistique (CREST), Ecole Nationale de la Statistique et de l'Analyse de l'Information [Bruz] (ENSAI)-École polytechnique (X)-École Nationale de la Statistique et de l'Administration Économique (ENSAE Paris)-Centre National de la Recherche Scientifique (CNRS), Institut de Recherche Mathématique de Rennes (IRMAR), AGROCAMPUS OUEST, Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Université de Rennes 2 (UR2), Université de Rennes (UNIV-RENNES)-École normale supérieure - Rennes (ENS Rennes)-Centre National de la Recherche Scientifique (CNRS)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA), Centre de Recherche en Économie et Statistique ( CREST ), Ecole Nationale de la Statistique et de l'Analyse de l'Information [Bruz] ( ENSAI ) -École polytechnique ( X ) -École Nationale de la Statistique et de l'Administration Économique ( ENSAE ParisTech ), Institut de Recherche Mathématique de Rennes ( IRMAR ), Université de Rennes 1 ( UR1 ), Université de Rennes ( UNIV-RENNES ) -Université de Rennes ( UNIV-RENNES ) -AGROCAMPUS OUEST-École normale supérieure - Rennes ( ENS Rennes ) -Institut National de Recherche en Informatique et en Automatique ( Inria ) -Institut National des Sciences Appliquées ( INSA ) -Université de Rennes 2 ( UR2 ), Université de Rennes ( UNIV-RENNES ) -Centre National de la Recherche Scientifique ( CNRS ), Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-École normale supérieure - Rennes (ENS Rennes)-Université de Rennes 2 (UR2)-Centre National de la Recherche Scientifique (CNRS)-INSTITUT AGRO Agrocampus Ouest, and Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)

Subjects
Statistics and Probability, Calibration (statistics), Variance approximation, 01 natural sciences, Horvitz–Thompson estimator, 010104 statistics & probability, [MATH.MATH-ST]Mathematics [math]/Statistics [math.ST], 0502 economics and business, Statistics, [ MATH.MATH-ST ] Mathematics [math]/Statistics [math.ST], 62D05, 0101 mathematics, Cube method, 050205 econometrics, Mathematics, Analysis of covariance, Balanced sampling, Applied Mathematics, 05 social sciences, Estimator, Regression analysis, Variance (accounting), [STAT.TH]Statistics [stat]/Statistics Theory [stat.TH], [ STAT.TH ] Statistics [stat]/Statistics Theory [stat.TH], Variable (computer science), Calibration, Probability distribution, Statistics, Probability and Uncertainty, Fixed-point algorithm
Abstract

International audience; When auxiliary information is available at the design stage, samples may be selected by means of balanced sampling. The variance of the Horvitz-Thompson estimator is then reduced, since it is approximately given by that of the residuals of the variable of interest on the balancing variables. In this paper, a method for computing optimal inclusion probabilities for balanced sampling on given auxiliary variables is studied. We show that the method formerly suggested by Tillé and Favre (2005) enables the computation of inclusion probabilities that lead to a decrease in variance under some conditions on the set of balancing variables. A disadvantage is that the target optimal inclusion probabilities depend on the variable of interest. If the needed quantities are unknown at the design stage, we propose to use estimates instead (e.g.,arising from a previous wave of the survey). A limited simulation study suggests that, under some conditions, our method performs better than the method of Tillé and Favre (2005)

Published
2011
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