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Articles de revues sur le sujet « Discrete response models »

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Auteur : Grafiati
Publié le 11 mars 2023

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1

Hilbe, Joseph M. « Creating Synthetic Discrete-response Regression Models ». Stata Journal : Promoting communications on statistics and Stata 10, no 1 (mars 2010) : 104–24. http://dx.doi.org/10.1177/1536867x1001000110.

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2

Fahrmeir, Ludwig, et Heinz Kaufmann. « Asymptotic inference in discrete response models ». Statistische Hefte 27, no 1 (décembre 1986) : 179–205. http://dx.doi.org/10.1007/bf02932567.

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3

Lien, Donald, et David Rearden. « Missing measurements in discrete response models ». Economics Letters 32, no 3 (mars 1990) : 231–35. http://dx.doi.org/10.1016/0165-1765(90)90103-8.

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4

McFadden, Daniel, et Kenneth Train. « Mixed MNL models for discrete response ». Journal of Applied Econometrics 15, no 5 (2000) : 447–70. http://dx.doi.org/10.1002/1099-1255(200009/10)15:5<447 ::aid-jae570>3.0.co;2-1.

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Terza, Joseph V. « Optimal discrete prediction in parametric binary response models ». Economics Letters 91, no 1 (avril 2006) : 72–75. http://dx.doi.org/10.1016/j.econlet.2005.10.017.

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6

Arabatzis, Alexandros A., et Timothy G. Gregoire. « Ordered and unordered multinomial response models : an application to assess loblolly pine merchantability ». Canadian Journal of Forest Research 21, no 2 (1 février 1991) : 261–68. http://dx.doi.org/10.1139/x91-032.

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Qualitative response models constitute a class of regression models used to predict one of a discrete number of mutually exclusive outcomes. These models differ from continuous regression models in that the response variable takes only discrete values. In forestry applications, the use of such models has been largely confined to mortality studies where the dependent variable is dichotomous. However, it is common in forestry to deal with variables that are either naturally discrete or continuous but recorded discretely. Consequently, there is a need for models that are appropriate for polychotomous dependent variables. Two models that appear to be suitable for forestry applications are presented, namely the ordered and unordered multinomial models, with emphasis on their theoretical justification, statistical inference, and model selection criteria. Using permanent plot data from loblolly pine (Pinustaeda L.) plantations on cutover, site-prepared areas throughout the southern United States, these models were fitted to assess the merchantability of loblolly pine trees. The results demonstrate the potential of qualitative response models for meaningful implementation in a variety of forestry applications and, also, for suggested topics for future research work.
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7

Schmittmann, Verena D., Conor V. Dolan, Han L. J. van der Maas et Michael C. Neale. « Discrete Latent Markov Models for Normally Distributed Response Data ». Multivariate Behavioral Research 40, no 4 (octobre 2005) : 461–88. http://dx.doi.org/10.1207/s15327906mbr4004_4.

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8

Donkin, Christopher, Robert M. Nosofsky, Jason M. Gold et Richard M. Shiffrin. « Discrete-slots models of visual working-memory response times. » Psychological Review 120, no 4 (2013) : 873–902. http://dx.doi.org/10.1037/a0034247.

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9

Carstea, A. S., A. Ramani, J. Satsuma, R. Willox et B. Grammaticos. « Continuous, discrete and ultradiscrete models of an inflammatory response ». Physica A : Statistical Mechanics and its Applications 364 (mai 2006) : 276–86. http://dx.doi.org/10.1016/j.physa.2005.08.073.

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10

Khan, Shakeeb, et Denis Nekipelov. « Information structure and statistical information in discrete response models ». Quantitative Economics 9, no 2 (2018) : 995–1017. http://dx.doi.org/10.3982/qe288.

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11

Dersimonian, Rebecca, et Stuart G. Baker. « Two-process models for discrete-time serial categorical response ». Statistics in Medicine 7, no 9 (septembre 1988) : 965–74. http://dx.doi.org/10.1002/sim.4780070908.

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Joe, Harry. « Accuracy of Laplace approximation for discrete response mixed models ». Computational Statistics & ; Data Analysis 52, no 12 (août 2008) : 5066–74. http://dx.doi.org/10.1016/j.csda.2008.05.002.

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13

Robertson, A. N., K. C. Park et K. F. Alvin. « Identification of Structural Dynamics Models Using Wavelet-Generated Impulse Response Data ». Journal of Vibration and Acoustics 120, no 1 (1 janvier 1998) : 261–66. http://dx.doi.org/10.1115/1.2893815.

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This paper addresses the use of discrete wavelet transforms for the identification of structural dynamics models. First, the discrete temporal impulse response functions are obtained from vibration records by the discrete wavelet transform (DWT). They are then utilized for system realizations. From the realized state space models, structural modes, mode shapes and damping parameters are extracted. Attention has been focused on a careful comparison of the present DWT system identification approach to the FFT-based approach. Numerical examples demonstrate that the present DWT-based structural system identification procedure is a serious alternative to the FFT-based procedure, and outperforms FFT methods for narrow frequency-band inputs.
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14

Haertel, Edward H. « Continuous and discrete latent structure models for item response data ». Psychometrika 55, no 3 (septembre 1990) : 477–94. http://dx.doi.org/10.1007/bf02294762.

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15

GOLDSTEIN, HARVEY. « Nonlinear multilevel models, with an application to discrete response data ». Biometrika 78, no 1 (1991) : 45–51. http://dx.doi.org/10.1093/biomet/78.1.45.

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16

Kheifets, Igor, et Carlos Velasco. « New goodness-of-fit diagnostics for conditional discrete response models ». Journal of Econometrics 200, no 1 (septembre 2017) : 135–49. http://dx.doi.org/10.1016/j.jeconom.2017.05.017.

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17

Kawai, Shin, et Noriyuki Hori. « MAPPING DISCRETE-TIME MODELS FOR DESCRIPTOR-SYSTEMS WITH CONSISTENT INITIAL CONDITIONS ». Transactions of the Canadian Society for Mechanical Engineering 40, no 1 (mars 2016) : 59–77. http://dx.doi.org/10.1139/tcsme-2016-0005.

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Discretization of a regular continuous-time descriptor-system, whose initial condition is consistent with its input, is considered using a general mapping method presented in our previous paper. The proposed mapping discrete-time model is shown to be a proper discretization under the definition explained in the paper. This assures that the response of the mapping model approaches that of the continuous-time descriptor system as the sampling period approaches zero. The consistency of initial conditions for the discrete-time model is also studied and the long-standing issue of ambiguities surrounding irregularities of discrete-time responses at the initial time are clarified with a simple solution. A proper range of design parameters are investigated and their suitable choices suggested. To illustrate the use of the proposed method, a simple circuit that cannot be expressed in the ordinary state-space form is considered. Its responses to a sinusoidal input when started from the consistent and inconsistent initial conditions are simulated to show that the irregularities at the initial time can be overcome easily. The proposed technique provides a convenient simulation and design environment for handling discrete-time systems in a unified manner with consistency and ease.
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18

Sapra, Sunil K. « Discrete choice models with response transformation : An application to beverage choice ». International Journal of Accounting and Economics Studies 3, no 2 (25 septembre 2015) : 135. http://dx.doi.org/10.14419/ijaes.v3i2.5317.

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<p>The paper studies various response transformation models for discrete choice and categorical data. These response transformation models are fitted to binary response data on beverage choice. Several models are compared, and the best model is selected using AICs and deviances. The transformations include extensions of the widely used Box-Cox transformation to Normality for continuous data to categorical data. The econometric techniques employed in the paper are widely applicable to the analysis of count, binary response, and duration types of data encountered in business and economics.</p>
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19

Batill, S. M., et J. J. Hollkamp. « Parameter identification of discrete-time series models for structural response prediction ». AIAA Journal 27, no 11 (novembre 1989) : 1636–43. http://dx.doi.org/10.2514/3.10312.

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Chaumette, Eric, Benoit Priot, Francois Vincent, Gael Pages et Arnaud Dion. « Minimum Variance Distortionless Response Estimators for Linear Discrete State-Space Models ». IEEE Transactions on Automatic Control 62, no 4 (avril 2017) : 2048–55. http://dx.doi.org/10.1109/tac.2016.2594384.

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21

Jäggi, Boris, Claude Weis et Kay W. Axhausen. « Stated response and multiple discrete-continuous choice models : Analyses of residuals ». Journal of Choice Modelling 6 (mars 2013) : 44–59. http://dx.doi.org/10.1016/j.jocm.2013.04.005.

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22

Azzalini, Adelchi, Hyoung-Moon Kim et Hea-Jung Kim. « Sample selection models for discrete and other non-Gaussian response variables ». Statistical Methods & ; Applications 28, no 1 (30 mars 2018) : 27–56. http://dx.doi.org/10.1007/s10260-018-0427-1.

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23

Hunt, Daniel L., Shesh N. Rai et Chin-Shang Li. « Summary of Dose-Response Modeling for Developmental Toxicity Studies ». Dose-Response 6, no 4 (1 octobre 2008) : dose—response.0. http://dx.doi.org/10.2203/dose-response.08-007.hunt.

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Developmental toxicity studies are an important area in the field of toxicology. Endpoints measured on fetuses include weight and indicators of death and malformation. Binary indicator measures are typically summed over the litter and a discrete distribution is assumed to model the number of adversely affected fetuses. Additionally, there is noticeable variation in the litter responses within dose groups that should be taken into account when modeling. Finally, the dose-response pattern in these studies exhibits a threshold effect. The threshold dose-response model is the default model for non-carcinogenic risk assessment, according to the USEPA, and is encouraged by the agency for the use in the risk assessment process. Two statistical models are proposed to estimate dose-response pattern of data from the developmental toxicity study: the threshold model and the spline model. The models were applied to two data sets. The advantages and disadvantages of these models, potential other models, and future research possibilities will be summarized.
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24

Mai, Tien, et Arunesh Sinha. « Choices Are Not Independent : Stackelberg Security Games with Nested Quantal Response Models ». Proceedings of the AAAI Conference on Artificial Intelligence 36, no 5 (28 juin 2022) : 5141–49. http://dx.doi.org/10.1609/aaai.v36i5.20448.

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The quantal response (QR) model is widely used in Stackelberg security games (SSG) to model a bounded rational adversary. The QR model is a model of human response from among a large variety of prominent models known as discrete choice models. QR is the simplest type of discrete choice models and does not capture commonly observed phenomenon such as correlation among choices. We introduce the nested QR adversary model (based on nested logit model in discrete choice theory) in SSG which addresses shortcoming of the QR model. We present tractable approximation of the resulting equilibrium problem with nested QR adversary. We do so by deriving an interesting property of the equilibrium problem, namely a loosely coupled split into nested problems that mirrors the nested decision making by the adversary in the nested QR model. We show that each separate nested problem can be approximated efficiently and that the loosely coupled overall problem can be solved approximately by formulating it as a discretized version of a continuous dynamic program. Finally, we conduct experiments that show the scalability and parallelizability of our approach, as well as advantages of the nested QR model.
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25

Otter, Thomas, Greg M. Allenby et Trish Van Zandt. « An Integrated Model of Discrete Choice and Response Time ». Journal of Marketing Research 45, no 5 (octobre 2008) : 593–607. http://dx.doi.org/10.1509/jmkr.45.5.593.

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Computer and Web-based interviewing tools have made response times ubiquitous in marketing research. Practitioners use these data as an indicator of data quality, and academics use them as an indicator of latent processes related to memory, attributes, and decision making. The authors investigate a Poisson race model with choice and response times as dependent variables. The model facilitates inference about respondents' preferences for choice alternatives, their diligence in providing responses, and the accessibility of attitudes and the speed of thinking. Thus, the model distinguishes between respondents who are quick to think and those who are quick to react but do so without much thought. Empirically, the authors find support for the endogenous nature of response times and demonstrate that models that treat response times as exogenous variables may result in misleading inferences.
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26

Ranger, Jochen, et Jörg-Tobias Kuhn. « Estimating Diffusion-Based Item Response Theory Models : Exploring the Robustness of Three Old and Two New Estimators ». Journal of Educational and Behavioral Statistics 43, no 6 (24 juillet 2018) : 635–62. http://dx.doi.org/10.3102/1076998618787791.

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Diffusion-based item response theory models for responses and response times in tests have attracted increased attention recently in psychometrics. Analyzing response time data, however, is delicate as response times are often contaminated by unusual observations. This can have serious effects on the validity of statistical inference. In this article, we compare three established and two new estimation approaches for diffusion-based item response theory models with respect to their robustness. The three established approaches are the marginal maximum likelihood (ML) estimator for continuous time, the marginal ML estimator for discrete time, and the weighted least squares (WLS) estimator. The new approaches are two modifications of the WLS estimator with better robustness properties. The performance of the estimators is compared in a simulation study. The simulation study illustrates that the new approaches are robust against some forms of random independent contamination. The marginal ML estimator for discrete time also performs well. The marginal ML estimator for continuous time is heavily affected by contamination.
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27

Baker, Myles L., et D. Lewis Mingori. « Removing bias from discrete time models based on finite interval response data ». Journal of Guidance, Control, and Dynamics 19, no 6 (novembre 1996) : 1216–20. http://dx.doi.org/10.2514/3.21774.

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28

Kuhnt, Sonja, Dominik Kirchhoff, Sigrid Wenzel et Jana Stolipin. « Generating Logistic Characteristic Curves using Discrete Event Simulation and Response Surface Models ». SNE Simulation Notes Europe 30, no 3 (septembre 2020) : 95–104. http://dx.doi.org/10.11128/sne.30.tn.10522.

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29

Chen, Songnian, et Shakeeb Khan. « Rates of convergence for estimating regression coefficients in heteroskedastic discrete response models ». Journal of Econometrics 117, no 2 (décembre 2003) : 245–78. http://dx.doi.org/10.1016/s0304-4076(03)00148-9.

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30

Hensher, David A., et Mark Bradley. « Using stated response choice data to enrich revealed preference discrete choice models ». Marketing Letters 4, no 2 (avril 1993) : 139–51. http://dx.doi.org/10.1007/bf00994072.

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Flippen, L. D. « Interpolation-based condensation of algebraic semi-discrete models with frequency response application ». Computers & ; Mathematics with Applications 29, no 9 (mai 1995) : 39–52. http://dx.doi.org/10.1016/0898-1221(95)00036-x.

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Xu, Xu Steven, Hui Wang et An Vermeulen. « Modeling delayed drug effect using discrete-time nonlinear autoregressive models : a connection with indirect response models ». Journal of Pharmacokinetics and Pharmacodynamics 38, no 3 (31 mars 2011) : 353–67. http://dx.doi.org/10.1007/s10928-011-9197-1.

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33

Nojkovic, Aleksandra. « Qualitative response models : A survey of methodology and illustrative applications ». Ekonomski anali 52, no 172 (2007) : 55–92. http://dx.doi.org/10.2298/eka0772055n.

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This paper introduces econometric modeling with discrete (categorical) dependent variables. Such models, commonly referred to as qualitative response (QR) models, have become a standard tool of microeconometric analysis. Microeconometric research represents empirical analysis of microdata, i.e. economic information about individuals, households and firms. Microeconometrics has been most widely adopted in various fields, such as labour economics, consumer behavior, or economy of transport. The latest research shows that this methodology can also be successfully transferred to macroeconomic context and applied to time series and panel data analysis in a wider scope. .
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34

Baranovsky, Serhii, et Andrij Bomba. « Step by Step Perturbation of Discrete Models of Immunology ». Mathematical and computer modelling. Series : Technical sciences 23 (6 décembre 2022) : 5–19. http://dx.doi.org/10.32626/2308-5916.2022-23.5-19.

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A number of very different mathematical models are used to predict the response of the immune system to pathogenic microorganisms detected in the body and the corresponding course of viral disease. Usually,such models are based on the assumption that the body is a homogeneous envi-ronment in which all factors are evenly distributed.The article presents a generalized discrete model of Marchuk's infectious disease for the complex accounting of small diffusion «redistributions», con-centrated effects and the body's temperature response. The introduction of such additional terms into the basic model significantly complicates the orig-inal problem and aggravates the problem of constructing efficient algorithms for the numerical solution of such systems of differential equations with de-lays. It is noted that as a result of discretization of the original model problem using an implicit scheme, a nonlinear system of equations is obtained, the so-lution of which must be sought at each time step by iterations. Thus, the use of the corresponding classical Runge-Kutta schemes is very uneconomical from the point of view of calculations.The authors propose a step-by-step procedure for numerically asymp-totic approximation of the solution of the corresponding singularly per-turbed discrete problem with delay, which allows to combine the ad-vantages of implicit schemes and the cost-effectiveness of explicit schemes. The results of computer simulations are presented, which illus-trate the influence of diffuse «scattering»of antigens, delays and concen-trated sources of antigens on the nature of the infectious disease. It is em-phasized that the complex action of these factors can lead to a reduction of the initially supercritical concentration of antigens to a more acceptable level, which is important in forming a rational program of decision-making on the use of external «therapeutic»effects.
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35

Hari Rao, V. Sree, et P. Raja Sekhara Rao. « Stability analysis of resource-consumer dynamic models ». ANZIAM Journal 47, no 3 (janvier 2006) : 413–38. http://dx.doi.org/10.1017/s1446181100009925.

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AbstractA nutrient-consumer model involving a distributed delay in material recycling and a discrete delay in growth response has been analysed. Various easily verifiable sets of sufficient conditions for global asymptotic stability of the positive equilibrium solution of the model equations have been obtained and the length of the delay in each case has been estimated.
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White, Anthony S., et Michael Censlive. « The Effects of Modelling Strategies on Responses of Inventory Models ». International Journal of Applied Industrial Engineering 4, no 1 (janvier 2017) : 19–43. http://dx.doi.org/10.4018/ijaie.2017010102.

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This paper describes methods to model inventories from the APVIOBPCS family. It aims to examine the limits of modelling approaches within control-theoretic models using the Simulink package. Discrete and continuous time models were considered together with a finite pipeline delay and the Forrester exponential delay, in continuous and sampled data representation. The main effect of using a finite delay is to deepen the stock-out and increase the required order rate compared with the same response observed with an exponential form delay. Total time for recovery is similar with all models. The discrete performances are close to the continuous representation for a smaller review period. Results presented here illustrate that the various forms of control-theoretic models present similar step response results irrespective of simulation technique used provided they have the same delay type. However, the gains required for minimum cost are substantially different for each delay form and modelling technique used.
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37

Cessac, Bruno, Ignacio Ampuero et Rodrigo Cofré. « Linear Response of General Observables in Spiking Neuronal Network Models ». Entropy 23, no 2 (27 janvier 2021) : 155. http://dx.doi.org/10.3390/e23020155.

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We establish a general linear response relation for spiking neuronal networks, based on chains with unbounded memory. This relation allow us to predict the influence of a weak amplitude time dependent external stimuli on spatio-temporal spike correlations, from the spontaneous statistics (without stimulus) in a general context where the memory in spike dynamics can extend arbitrarily far in the past. Using this approach, we show how the linear response is explicitly related to the collective effect of the stimuli, intrinsic neuronal dynamics, and network connectivity on spike train statistics. We illustrate our results with numerical simulations performed over a discrete time integrate and fire model.
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Singh, Abhyudai. « A comparative approach to stabilizing mechanisms between discrete- and continuous-time consumer-resource models ». PLOS ONE 17, no 4 (12 avril 2022) : e0265825. http://dx.doi.org/10.1371/journal.pone.0265825.

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There is rich literature on using continuous-time and discrete-time models for studying population dynamics of consumer-resource interactions. A key focus of this contribution is to systematically compare between the two modeling formalisms the stabilizing/destabilizing impacts of diverse ecological processes that result in a density-dependent consumer attack rate. Inspired by the Nicholson-Bailey/Lotka-Volterra models in discrete-time/continuous-time, respectively, we consider host-parasitoid interactions with an arbitrary parasitoid attack rate that is a function of both the host/parasitoid population densities. Our analysis shows that a Type II functional response is stabilizing in both modeling frameworks only when combined with other mechanisms, such as mutual interference between parasitoids. A Type III functional response is by itself stabilizing, but the extent of attack-rate acceleration needed is much higher in the discrete-time framework, and its stability regime expands with increasing host reproduction. Finally, our results show that while mutual parasitoid interference can stabilize population dynamics, cooperation between parasitoids to handle hosts is destabilizing in both frameworks. In summary, our comparative analysis systematically characterizes diverse ecological processes driving stable population dynamics in discrete-time and continuous-time consumer-resource models.
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39

Ferber, Kyle, et Kellie J. Archer. « Modeling Discrete Survival Time Using Genomic Feature Data ». Cancer Informatics 14s2 (janvier 2015) : CIN.S17275. http://dx.doi.org/10.4137/cin.s17275.

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Researchers have recently shown that penalized models perform well when applied to high-throughput genomic data. Previous researchers introduced the generalized monotone incremental forward stagewise (GMIFS) method for fitting overparameterized logistic regression models. The GMIFS method was subsequently extended by others for fitting several different logit link ordinal response models to high-throughput genomic data. In this study, we further extended the GMIFS method for ordinal response modeling using a complementary log-log link, which allows one to model discrete survival data. We applied our extension to a publicly available microarray gene expression dataset (GSE53733) with a discrete survival outcome. The dataset included 70 primary glioblastoma samples from patients of the German Glioma Network with long-, intermediate-, and short-term overall survival. We tested the performance of our method by examining the prediction accuracy of the fitted model. The method has been implemented as an addition to the ordinalgmifs package in the R programming environment.
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Price, William J., Bahman Shafii et Steven S. Seefeldt. « Estimation of Dose–Response Models for Discrete and Continuous Data in Weed Science ». Weed Technology 26, no 3 (septembre 2012) : 587–601. http://dx.doi.org/10.1614/wt-d-11-00101.1.

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Dose–response analysis is widely used in biological sciences and has application to a variety of risk assessment, bioassay, and calibration problems. In weed science, dose–response methodologies have typically relied on least squares estimation under the assumptions of normal, homoscedastic, and independent errors. Advances in computational abilities and available software, however, have given researchers more flexibility and choices for data analysis when these assumptions are not appropriate. This article will explore these techniques and demonstrate their use to provide researchers with an up-to-date set of tools necessary for analysis of dose–response problems. Demonstrations of the techniques are provided using a variety of data examples from weed science.
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41

Bhaduri, Anindya, Christopher S. Meyer, John W. Gillespie, Bazle Z. “Gama” Haque, Michael D. Shields et Lori Graham-Brady. « Probabilistic Modeling of Discrete Structural Response with Application to Composite Plate Penetration Models ». Journal of Engineering Mechanics 147, no 11 (novembre 2021) : 04021087. http://dx.doi.org/10.1061/(asce)em.1943-7889.0001996.

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42

Tutz, Gerhard. « Competing risks models in discrete time with nominal or ordinal categories of response ». Quality & ; Quantity 29, no 4 (novembre 1995) : 405–20. http://dx.doi.org/10.1007/bf01106065.

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43

Alpízar, Geisel, et Luis F. Gordillo. « Disease Spread in Coupled Populations : Minimizing Response Strategies Costs in Discrete Time Models ». Discrete Dynamics in Nature and Society 2013 (2013) : 1–9. http://dx.doi.org/10.1155/2013/681689.

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Social distancing, vaccination, and medical treatments have been extensively studied and widely used to control the spread of infectious diseases. However, it is still a difficult task for health administrators to determine the optimal combination of these strategies when confronting disease outbreaks with limited resources, especially in the case of interconnected populations, where the flow of individuals is usually restricted with the hope of avoiding further contamination. We consider two coupled populations and examine them independently under two variants of well-known discrete time disease models. In both examples we compute approximations for the control levels necessary to minimize costs and quickly contain outbreaks. The main technique used is simulated annealing, a stochastic search optimization tool that, in contrast with traditional analytical methods, allows easy implementation to any number of patches with different kinds of couplings and internal dynamics.
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Shmaliy, Yuriy S., et Oscar Ibarra-Manzano. « Time-variant linear optimal finite impulse response estimator for discrete state-space models ». International Journal of Adaptive Control and Signal Processing 26, no 2 (20 septembre 2011) : 95–104. http://dx.doi.org/10.1002/acs.1274.

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Johnson, Timothy R. « Discrete Choice Models for Ordinal Response Variables : A Generalization of the Stereotype Model ». Psychometrika 72, no 4 (28 juillet 2007) : 489–504. http://dx.doi.org/10.1007/s11336-007-9020-5.

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FRANK, T. D., et S. MONGKOLSAKULVONG. « ON STRONGLY NONLINEAR AUTOREGRESSIVE MODELS : IMPLICATIONS FOR THE THEORY OF TRANSIENT AND STATIONARY RESPONSES OF MANY-BODY SYSTEMS ». Fluctuation and Noise Letters 12, no 04 (décembre 2013) : 1350022. http://dx.doi.org/10.1142/s0219477513500223.

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Two widely used concepts in physics and the life sciences are combined: mean field theory and time-discrete time series modeling. They are merged within the framework of strongly nonlinear stochastic processes, which are processes whose stochastic evolution equations depend self-consistently on process expectation values. Explicitly, a generalized autoregressive (AR) model is presented for an AR process that depends on its process mean value. Criteria for stationarity are derived. The transient dynamics in terms of the relaxation of the first moment and the stationary response to fluctuations in terms of the autocorrelation function are discussed. It is shown that due to the stochastic feedback via the process mean, transient and stationary responses may exhibit qualitatively different temporal patterns. That is, the model offers a time-discrete description of many-body systems that in certain parameter domains feature qualitatively different transient and stationary response dynamics.
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Kulikov, G. M. « Computational Models for Multilayered Composite Shells with Application to Tires ». Tire Science and Technology 24, no 1 (1 janvier 1996) : 11–38. http://dx.doi.org/10.2346/1.2137509.

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Abstract This paper focuses on four tire computational models based on two-dimensional shear deformation theories, namely, the first-order Timoshenko-type theory, the higher-order Timoshenko-type theory, the first-order discrete-layer theory, and the higher-order discrete-layer theory. The joint influence of anisotropy, geometrical nonlinearity, and laminated material response on the tire stress-strain fields is examined. The comparative analysis of stresses and strains of the cord-rubber tire on the basis of these four shell computational models is given. Results show that neglecting the effect of anisotropy leads to an incorrect description of the stress-strain fields even in bias-ply tires.
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Chesher, Andrew. « SEMIPARAMETRIC STRUCTURAL MODELS OF BINARY RESPONSE : SHAPE RESTRICTIONS AND PARTIAL IDENTIFICATION ». Econometric Theory 29, no 2 (30 juillet 2012) : 231–66. http://dx.doi.org/10.1017/s0266466612000321.

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I study the partial identifying power of structural single-equation threshold-crossing models for binary responses when explanatory variables may be endogenous. The sharp identified set of threshold functions is derived for the case in which explanatory variables are discrete, and I provide a constructive proof of sharpness. There is special attention to a widely employed semiparametric shape restriction, which requires the threshold-crossing function to be a monotone function of a linear index involving the observable explanatory variables. The restriction brings great computational benefits, allowing calculation of the identified set of index coefficients without calculating the nonparametrically specified threshold function. With the restriction in place, the methods of the paper can be applied to produce identified sets in a class of binary response models with mismeasured explanatory variables.
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Gørgens, Tue, et Dean Hyslop. « The Specification of Dynamic Discrete-Time Two-State Panel Data Models ». Econometrics 7, no 1 (24 décembre 2018) : 1. http://dx.doi.org/10.3390/econometrics7010001.

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This paper compares two approaches to analyzing longitudinal discrete-time binary outcomes. Dynamic binary response models focus on state occupancy and typically specify low-order Markovian state dependence. Multi-spell duration models focus on transitions between states and typically allow for state-specific duration dependence. We show that the former implicitly impose strong and testable restrictions on the transition probabilities. In a case study of poverty transitions, we show that these restrictions are severely rejected against the more flexible multi-spell duration models.
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GUO, YUZHU, L. Z. GUO, S. A. BILLINGS, DANIEL COCA et Z. Q. LANG. « CHARACTERIZING NONLINEAR SPATIO-TEMPORAL SYSTEMS IN THE FREQUENCY DOMAIN ». International Journal of Bifurcation and Chaos 22, no 02 (février 2012) : 1230009. http://dx.doi.org/10.1142/s0218127412300091.

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In this paper a new concept, spatio-temporal generalized frequency response functions (STGFRF), is introduced for the first time to characterize nonlinear spatio-temporal dynamical systems in the frequency domain. A probing method is developed to calculate the STGFRFs recursively for both continuous and discrete spatio-temporal systems. The algorithm is computationally compact and exposes the explicit relationship between the continuous and discrete models and the elements of the generalized frequency response functions.
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