Relevant bibliographies by topics / Time series regression

Academic literature on the topic 'Time series regression'

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Published: 4 June 2021
Last updated: 30 January 2023

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Journal articles on the topic "Time series regression"

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Tan, Chang Wei, Christoph Bergmeir, François Petitjean, and Geoffrey I. Webb. "Time series extrinsic regression." Data Mining and Knowledge Discovery 35, no. 3 (March 11, 2021): 1032–60. http://dx.doi.org/10.1007/s10618-021-00745-9.

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Truong, Young K., and Charles J. Stone. "SEMIPARAMETRIC TIME SERIES REGRESSION." Journal of Time Series Analysis 15, no. 4 (July 1994): 405–28. http://dx.doi.org/10.1111/j.1467-9892.1994.tb00202.x.

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Truong, Young K. "Nonparametric time series regression." Annals of the Institute of Statistical Mathematics 46, no. 2 (June 1994): 279–93. http://dx.doi.org/10.1007/bf01720585.

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Feng, Yanming. "Regression and Hypothesis Tests for Multivariate GNSS State Time Series." Journal of Global Positioning Systems 11, no. 1 (June 30, 2012): 33–45. http://dx.doi.org/10.5081/jgps.11.1.33.

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Choudhury, Askar H., Robert Hubata, and Robert D. St Louis. "Understanding Time-Series Regression Estimators." American Statistician 53, no. 4 (November 1999): 342. http://dx.doi.org/10.2307/2686054.

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Brännäs, Kurt, and Per Johansson. "Time series count data regression." Communications in Statistics - Theory and Methods 23, no. 10 (January 1994): 2907–25. http://dx.doi.org/10.1080/03610929408831424.

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Choudhury, Askar H., Robert Hubata, and Robert D. St. Louis. "Understanding Time-Series Regression Estimators." American Statistician 53, no. 4 (November 1999): 342–48. http://dx.doi.org/10.1080/00031305.1999.10474487.

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Jaditz, Ted, and Leigh A. Riddick. "Time-Series Near-Neighbor Regression." Studies in Nonlinear Dynamics and Econometrics 4, no. 1 (April 1, 2000): 35–44. http://dx.doi.org/10.1162/108118200569171.

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Cai, Zongwu. "REGRESSION QUANTILES FOR TIME SERIES." Econometric Theory 18, no. 1 (February 2002): 169–92. http://dx.doi.org/10.1017/s0266466602181096.

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In this paper we study nonparametric estimation of regression quantiles for time series data by inverting a weighted Nadaraya–Watson (WNW) estimator of conditional distribution function, which was first used by Hall, Wolff, and Yao (1999, Journal of the American Statistical Association 94, 154–163). First, under some regularity conditions, we establish the asymptotic normality and weak consistency of the WNW conditional distribution estimator for α-mixing time series at both boundary and interior points, and we show that the WNW conditional distribution estimator not only preserves the bias, variance, and, more important, automatic good boundary behavior properties of local linear “double-kernel” estimators introduced by Yu and Jones (1998, Journal of the American Statistical Association 93, 228–237), but also has the additional advantage of always being a distribution itself. Second, it is shown that under some regularity conditions, the WNW conditional quantile estimator is weakly consistent and normally distributed and that it inherits all good properties from the WNW conditional distribution estimator. A small simulation study is carried out to illustrate the performance of the estimates, and a real example is also used to demonstrate the methodology.
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Mammen, E., J. P. Nielsen, and B. Fitzenberger. "Generalized linear time series regression." Biometrika 98, no. 4 (October 13, 2011): 1007–14. http://dx.doi.org/10.1093/biomet/asr044.

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Dissertations / Theses on the topic "Time series regression"

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Clark, Allan Ernest. "Model selection-regression and time series applications." Master's thesis, University of Cape Town, 2003. http://hdl.handle.net/11427/18422.

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In any statistical analysis the researcher is often faced with the challenging task of gleaning relevant information from a sample data set in order to answer questions about the area under investigation. Often the exact data generating process that governs any data set is unknown, indicating that we have to estimate the data generating process by using statistical methods. Regression analysis and time series analysis are two statistical techniques that can be used to undertake such an analysis. In practice researcher will propose one model or a group of competing models that attempts to explain the data being investigated. This process is known as model selection. Model selection techniques have been developed to aid researchers in finding a suitable approximation to the true data generating process. Methods have also been developed that attempt to distinguish between different competing models. Many of these techniques entail using an information criterion that estimates the "closeness" of a fitted model to the unknown data generating process. This study investigates the properties of Bozdogan's Information complexity measure (ICOMP) when undertaking time series and regression analysis. Model selection techniques have been developed for both time series and regression analysis. The regression analysis techniques however often provide unsatisfactory results due to poor experimental designs. Poor experimental design could induce collinearities causing parameter estimates to become unstable with large standard errors. Time series analysis utilizes lagged autocorrelation- and lagged partial autocorrelation coefficients in order to specify the lag structure of the model. In certain data sets this process is not informative in determining the order of an ARIMA model. ICOMP guards against collinearity by considering the interaction between the parameters being estimated in a model. This study investigates the properties of ICOMP when undertaking regression and time series analysis by means of a simulation study. Bibliography: pages 250-263.
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Wu, Ying-keh. "Empirical Bayes procedures in time series regression models." Diss., Virginia Polytechnic Institute and State University, 1986. http://hdl.handle.net/10919/76089.

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In this dissertation empirical Bayes estimators for the coefficients in time series regression models are presented. Due to the uncontrollability of time series observations, explanatory variables in each stage do not remain unchanged. A generalization of the results of O'Bryan and Susarla is established and shown to be an extension of the results of Martz and Krutchkoff. Alternatively, as the distribution function of sample observations is hard to obtain except asymptotically, the results of Griffin and Krutchkoff on empirical linear Bayes estimation are extended and then applied to estimating the coefficients in time series regression models. Comparisons between the performance of these two approaches are also made. Finally, predictions in time series regression models using empirical Bayes estimators and empirical linear Bayes estimators are discussed.
Ph. D.
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Kidzinski, Lukasz. "Inference for stationary functional time series: dimension reduction and regression." Doctoral thesis, Universite Libre de Bruxelles, 2014. http://hdl.handle.net/2013/ULB-DIPOT:oai:dipot.ulb.ac.be:2013/209226.

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Les progrès continus dans les techniques du stockage et de la collection des données permettent d'observer et d'enregistrer des processus d’une façon presque continue. Des exemples incluent des données climatiques, des valeurs de transactions financières, des modèles des niveaux de pollution, etc. Pour analyser ces processus, nous avons besoin des outils statistiques appropriés. Une technique très connue est l'analyse de données fonctionnelles (ADF).

L'objectif principal de ce projet de doctorat est d'analyser la dépendance temporelle de l’ADF. Cette dépendance se produit, par exemple, si les données sont constituées à partir d'un processus en temps continu qui a été découpé en segments, les jours par exemple. Nous sommes alors dans le cadre des séries temporelles fonctionnelles.

La première partie de la thèse concerne la régression linéaire fonctionnelle, une extension de la régression multivariée. Nous avons découvert une méthode, basé sur les données, pour choisir la dimension de l’estimateur. Contrairement aux résultats existants, cette méthode n’exige pas d'assomptions invérifiables.

Dans la deuxième partie, on analyse les modèles linéaires fonctionnels dynamiques (MLFD), afin d'étendre les modèles linéaires, déjà reconnu, dans un cadre de la dépendance temporelle. Nous obtenons des estimateurs et des tests statistiques par des méthodes d’analyse harmonique. Nous nous inspirons par des idées de Brillinger qui a étudié ces models dans un contexte d’espaces vectoriels.
Doctorat en Sciences
info:eu-repo/semantics/nonPublished

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余瑞心 and Sui-sum Amy Yu. "Application of Markov regression models in non-Gaussian time series analysis." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 1991. http://hub.hku.hk/bib/B31976840.

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Yan, Ka-lok, and 忻嘉樂. "Time series regression modelling of air quality data in Hong Kong." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 1994. http://hub.hku.hk/bib/B31252990.

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Dehoky, Dylan, and Edward Sikorski. "Understanding and Exploiting commodity currencies : A Study using time series Regression." Thesis, KTH, Matematisk statistik, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-210167.

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This thesis within Industrial Economics and Applied Mathematics examines the term commodity currency. The thesis delves into analysing the characteristics and consequences of such a currency through a macroeconomic perspective while discussing previous studies within the matter. The applied mathematical statistics section audits the correlation between the currency and the commodities of the exporting country through a time series regression. The regression is based on the currency as the dependent variable and the commodities represent the covariates. Furthermore, a trading strategy is developed to see if a profit can be made on the foreign exchange market when looking at the commodity price movements.
Det här kandidatexamensarbetet är skrivet inom industriell ekonomi och tillämpad matematik och granskar termen råvaruvaluta (commodity currency). Uppsatsen analyserar, utifrån ett makroekonomiskt perspektiv, karaktärsdragen och konsekvenserna av en sådan valuta, samtidigt som den diskuterar tidigare studier inom ämnet. Delen inom tillämpad matematik undersöker korrelationen mellan valutan och råvarorna som landet exporterar genom en tidsserieregression. Regressionen är baserad på valutan som responsvariabel samtidigt som råvarorna representerar kovariaterna. Den färdiga modellen används sedan i en handelsstrategi som försöker förutspå växelkursens rörelser genom att titta på råvarornas rörelser.
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Herath, Herath Mudiyanselage Wiranthe Bandara. "TENSOR REGRESSION AND TENSOR TIME SERIES ANALYSES FOR HIGH DIMENSIONAL DATA." OpenSIUC, 2019. https://opensiuc.lib.siu.edu/theses/2585.

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Many real data are naturally represented as a multidimensional array called a tensor. In classical regression and time series models, the predictors and covariate variables are considered as a vector. However, due to high dimensionality of predictor variables, these types of models are inefficient for analyzing multidimensional data. In contrast, tensor structured models use predictors and covariate variables in a tensor format. Tensor regression and tensor time series models can reduce high dimensional data to a low dimensional framework and lead to efficient estimation and prediction. In this thesis, we discuss the modeling and estimation procedures for both tensor regression models and tensor time series models. The results of simulation studies and a numerical analysis are provided.
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Edlund, Per-Olov. "Preliminary estimation of transfer function weights : a two-step regression approach." Doctoral thesis, Stockholm : Economic Research Institute, Stockholm School of Economics [Ekonomiska forskningsinstitutet vid Handelshögsk.] (EFI), 1989. http://www.hhs.se/efi/summary/291.htm.

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Maharesi, Retno. "Modelling time series using time varying coefficient autoregressive models : with application to several data sets." Thesis, Edith Cowan University, Research Online, Perth, Western Australia, 1994. https://ro.ecu.edu.au/theses/1099.

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In this thesis the state space approach and the Kalman recursions are used for modelling univariate time series data. The models that are examined in this thesis are time varying Coefficient Autoregressive models, which can be represented in state space form. The coefficients are assumed to change according to a stationary process, a non-stationary process or a random process. In order to be able to estimate these changing unknown coefficients, they will be treated as state variables and the equation describing the changes of the state variables will be given by the state equation. The model can then be expressed in the form of a measurement equation. The parameters of the model, which include the transition matrix T, the covariance matrices of the random terms in the state equation and the measurement equation denoted respectively by Q and R will be obtained using the EM algorithm developed by Shumway and Stoffer (1982). Other models considered for comparison in this thesis are the Box-Jenkins and Harvey's Structural models. The results of model fitting are illustrated by applying these three models to three special data sets. These results are compared to investigate whether the time varying coefficients model can provide a better fit, and, where appropriate, a suitable data -transformation is applied to the data sets in order to get a fit of the time varying coefficient autoregressive model.
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Hyung, Namwon. "Essays on panel and nonlinear time series analysis /." Diss., Connect to a 24 p. preview or request complete full text in PDF format. Access restricted to UC campuses, 1999. http://wwwlib.umi.com/cr/ucsd/fullcit?p9958858.

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Books on the topic "Time series regression"

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Konstantinos, Fokianos, ed. Regression models for time series analysis. Hoboken, N.J: Wiley-Interscience, 2002.

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Ostrom, Charles W. Time series analysis: Regression techniques. 2nd ed. Newberry Park, Calif: Sage Publications, 1990.

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Hylleberg, Svend. Seasonality in regression. Orlando: Academic Press, 1986.

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Chih-Ling, Tsai, ed. Regression and time series model selection. Singapore: World Scientific, 1998.

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Kedem, Benjamin, and Konstantinos Fokianos. Regression Models for Time Series Analysis. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2002. http://dx.doi.org/10.1002/0471266981.

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Štulajter, František. Predictions in time series using regression models. New York: Springer, 2002.

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Seasonality in regression. Orlando, Fla: Academic Press, 1986.

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Fuller, Wayne A. Introduction to statistical time series. 2nd ed. New York: Wiley, 1996.

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Fuller, Wayne A. Introduction to statistical time series. 2nd ed. New York: Wiley, 1996.

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Robinson, P. M. Time series regression with long range dependence. London: Suntory and Toyota International Centres for Economics and Related Disciplines, 1997.

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Book chapters on the topic "Time series regression"

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Arkes, Jeremy. "Time-series models." In Regression Analysis, 287–314. 2nd ed. London: Routledge, 2022. http://dx.doi.org/10.4324/9781003285007-10.

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Wei, William W. S. "Time Series Regression." In International Encyclopedia of Statistical Science, 1607–9. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-04898-2_596.

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Woodward, Wayne A., Bivin P. Sadler, and Stephen D. Robertson. "Time Series Regression." In Time Series for Data Science, 343–80. New York: Chapman and Hall/CRC, 2022. http://dx.doi.org/10.1201/9781003089070-8.

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Foster, Dean P., Robert A. Stine, and Richard P. Waterman. "Modeling Time Series." In Business Analysis Using Regression, 299–332. New York, NY: Springer New York, 1998. http://dx.doi.org/10.1007/978-1-4612-0683-5_12.

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Cowpertwait, Paul S. P., and Andrew V. Metcalfe. "Regression." In Introductory Time Series with R, 91–120. New York, NY: Springer New York, 2009. http://dx.doi.org/10.1007/978-0-387-88698-5_5.

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Uribe, Jorge M., and Montserrat Guillen. "Time Series Quantile Regression." In Quantile Regression for Cross-Sectional and Time Series Data, 33–44. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-44504-1_5.

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Kock, Anders Bredahl, Marcelo Medeiros, and Gabriel Vasconcelos. "Penalized Time Series Regression." In Macroeconomic Forecasting in the Era of Big Data, 193–228. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-31150-6_7.

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Zivot, Eric, and Jiahui Wang. "Time Series Regression Modeling." In Modeling Financial Time Series with S-Plus®, 167–207. New York, NY: Springer New York, 2003. http://dx.doi.org/10.1007/978-0-387-21763-5_6.

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Ravishanker, Nalini, Balaji Raman, and Refik Soyer. "Time Series Regression Models." In Dynamic Time Series Models using R-INLA, 95–110. Boca Raton: Chapman and Hall/CRC, 2022. http://dx.doi.org/10.1201/9781003134039-5.

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Zong, Ping. "Time Series Regression Analysis." In The Art and Science of Econometrics, 97–126. London: Routledge, 2022. http://dx.doi.org/10.4324/9781003273905-5.

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Conference papers on the topic "Time series regression"

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Wen Gu, Baifeng Li, Baolong Niu, Wei Wei, and Zhiming Zheng. "Time series regression and prediction based on boosting regression." In 2014 IEEE Workshop on Advanced Research and Technology in Industry Applications (WARTIA). IEEE, 2014. http://dx.doi.org/10.1109/wartia.2014.6976244.

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Neto, João B. Pinto, Nathalie Mitton, Miguel Elias M. Campista, and Luís Henrique M. K. Costa. "Dead reckoning using time series regression models." In the 4th ACM MobiHoc Workshop. New York, New York, USA: ACM Press, 2018. http://dx.doi.org/10.1145/3213299.3213305.

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Yoshikawa, Hiroki, Akira Uchiyama, and Teruo Higashino. "Time-Series Physiological Data Balancing for Regression." In 2021 IEEE International Conference on Artificial Intelligence and Computer Applications (ICAICA). IEEE, 2021. http://dx.doi.org/10.1109/icaica52286.2021.9498128.

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"Interpretable Classification And Regression For Time Series Data." In 2020 28th Signal Processing and Communications Applications Conference (SIU). IEEE, 2020. http://dx.doi.org/10.1109/siu49456.2020.9302352.

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Shah, Rakshit, Poojan Shah, Catherene Joshi, Rutuja Jain, and Rushikesh Nikam. "Linear Regression vs LSTM for Time Series Data." In 2022 IEEE World Conference on Applied Intelligence and Computing (AIC). IEEE, 2022. http://dx.doi.org/10.1109/aic55036.2022.9848887.

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Pavao, Adrien, Isabelle Guyon, Nachar Stephane, Fabrice Lebeau, Martin Ghienne, Ludovic Platon, Tristan Barbagelata, et al. "Aircraft Numerical “Twin”: A Time Series Regression Competition." In 2021 20th IEEE International Conference on Machine Learning and Applications (ICMLA). IEEE, 2021. http://dx.doi.org/10.1109/icmla52953.2021.00075.

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Mukhaiyar, Utriweni, Debby Masteriana, and Mila Isti Riani. "The outlier detection in time series regression model." In THE 8TH INTERNATIONAL CONFERENCE AND WORKSHOP ON BASIC AND APPLIED SCIENCE (ICOWOBAS) 2021. AIP Publishing, 2023. http://dx.doi.org/10.1063/5.0104584.

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Priya, S. Selva, and Lavanya Gupta. "Predicting the future in time series using auto regressive linear regression modeling." In 2015 Twelfth International Conference on Wireless and Optical Communications Networks (WOCN). IEEE, 2015. http://dx.doi.org/10.1109/wocn.2015.8064521.

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Ristanoski, Goce, Wei Liu, and James Bailey. "A time-dependent enhanced support vector machine for time series regression." In KDD' 13: The 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York, NY, USA: ACM, 2013. http://dx.doi.org/10.1145/2487575.2487655.

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Lin, Kunhui, Qiang Lin, Changle Zhou, and Junfeng Yao. "Time Series Prediction Based on Linear Regression and SVR." In Third International Conference on Natural Computation (ICNC 2007). IEEE, 2007. http://dx.doi.org/10.1109/icnc.2007.780.

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Reports on the topic "Time series regression"

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Vogt, Michael. Nonparametric regression for locally stationary time series. Cemmap, September 2012. http://dx.doi.org/10.1920/wp.cem.2012.2212.

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Elliott, Graham, and James Stock. Inference in Time Series Regression When the Order of Integration of a Regressor is Unknown. Cambridge, MA: National Bureau of Economic Research, June 1992. http://dx.doi.org/10.3386/t0122.

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Villamizar-Villegas, Mauricio, and Yasin Kursat Onder. Uncovering Time-Specific Heterogeneity in Regression Discontinuity Designs. Banco de la República de Colombia, November 2020. http://dx.doi.org/10.32468/be.1141.

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The literature that employs Regression Discontinuity Designs (RDD) typically stacks data across time periods and cutoff values. While practical, this procedure omits useful time heterogeneity. In this paper we decompose the RDD treatment effect into its weighted time-value parts. This analysis adds richness to the RDD estimand, where each time-specific component can be different and informative in a manner that is not expressed by the single cutoff or pooled regressions. To illustrate our methodology, we present two empirical examples: one using repeated cross-sectional data and another using time-series. Overall, we show a significant heterogeneity in both cutoff and time-specific effects. From a policy standpoint, this heterogeneity can pick up key differences in treatment across economically relevant episodes. Finally, we propose a new estimator that uses all observations from the original design and which captures the incremental effect of policy given a state variable. We show that this estimator is generally more precise compared to those that exclude observations exposed to other cutoffs or time periods. Our proposed framework is simple and easily replicable and can be applied to any RDD application that carries an explicitly traceable time dimension.
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Gradín, Carlos. WIID Companion (March 2021): integrated and standardized series. UNU-WIDER, March 2021. http://dx.doi.org/10.35188/unu-wider/wtn/2021-5.

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This document is part of a series of technical notes describing the compilation of a new companion database that complements the World Income Inequality Database. It aims at facilitating the analysis of inequality as well as progress in achieving the global goal of reducing inequality within and across countries. This new dataset also includes an annual series reporting the income distribution at the percentile level for all citizens in the world, regardless of where they live, since 1950 to present. A previous note described the selection of income distribution series. Since these series may differ across welfare concepts and other methods used, this technical note describes the second stage, constructing integrated and standardized country series. It discusses all the necessary adjustments conducted to construct the final series for each country, with consistent estimates of the distribution of net income per capita over the entire period for which information is available. This is mainly divided into two stages. First, integrating country series by interlinking series that overlap over time, then using a more general regression-based approach.
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Bohorquez-Penuela, Camilo, and Mariana Urbina-Ramirez. Rising Staple Prices and Food Insecurity: The Case of the Mexican Tortilla. Banco de la República de Colombia, November 2020. http://dx.doi.org/10.32468/be.1144.

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We study the relationship between rising prices of tortillas---the Mexican staple par excellence---and household food insecurity between 2008 and 2014, a period in which global food prices experienced dramatic increases. The use of a unique combination of household-level data and official state-level information on prices allows us exploit signi cant variation in prices across the Mexican states. Since households cannot be tracked across time, we follow Deaton (1985) by constructing a series of pseudo-panels to control for time- invariant unobserved heterogeneity and measurement error. The regression estimates suggest that increasing tortilla prices affected food insecurity rates in Mexico. More speci cally, households with children or those in the second or third income quintile are more likely to be affected.
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Wolf, Christian, and Alisdair McKay. What Can Time-Series Regressions Tell Us About Policy Counterfactuals? Cambridge, MA: National Bureau of Economic Research, August 2022. http://dx.doi.org/10.3386/w30358.

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Lu, Tianjun, Jian-yu Ke, Fynnwin Prager, and Jose N. Martinez. “TELE-commuting” During the COVID-19 Pandemic and Beyond: Unveiling State-wide Patterns and Trends of Telecommuting in Relation to Transportation, Employment, Land Use, and Emissions in Calif. Mineta Transportation Institute, August 2022. http://dx.doi.org/10.31979/mti.2022.2147.

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Telecommuting, the practice of working remotely at home, increased significantly (25% to 35%) early in the COVID-19 pandemic. This shift represented a major societal change that reshaped the family, work, and social lives of many Californians. These changes also raise important questions about what factors influenced telecommuting before, during, and after COVID-19, and to what extent changes in telecommuting have influenced transportation patterns across commute modes, employment, land use, and environment. The research team conducted state-level telecommuting surveys using a crowd-sourced platform (i.e., Amazon Mechanical Turk) to obtain valid samples across California (n=1,985) and conducted state-level interviews among stakeholders (n=28) across ten major industries in California. The study leveraged secondary datasets and developed regression and time-series models. Our surveys found that, compared to pre-pandemic levels, more people had a dedicated workspace at home and had received adequate training and support for telecommuting, became more flexible to choose their own schedules, and had improved their working performance—but felt isolated and found it difficult to separate home and work life. Our interviews suggested that telecommuting policies were not commonly designed and implemented until COVID-19. Additionally, regression analyses showed that telecommuting practices have been influenced by COVID-19 related policies, public risk perception, home prices, broadband rates, and government employment. This study reveals advantages and disadvantages of telecommuting and unveils the complex relationships among the COVID-19 outbreak, transportation systems, employment, land use, and emissions as well as public risk perception and economic factors. The study informs statewide and regional policies to adapt to the new patterns of telecommuting.
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8

Kan, Marni L., Hsiu Chen Yeh, Lisa M. Schainker, Jessica Nelson, Samantha Charm, Cleve Redmond, and Richard Spoth. Substance Misuse Prevention Program Attendance: Predictors Among Military Families. RTI Press, December 2022. http://dx.doi.org/10.3768/rtipress.2022.rr.0048.2212.

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Typical life circumstances for military families may impact their participation in prevention programs, yet little is known about what factors influence their participation. The current study examined predictors of attendance in the Strengthening Families Program: For Parents and Youth 10–14, for Military Families, a universal in-person program designed to improve family functioning and reduce youth substance misuse and other problem behaviors. Participants included 159 parent–child dyads randomly selected to be offered the 7-week family program. Analyses examined demographic characteristics, deployment experiences, time spent waiting for the program to begin, and psychosocial functioning as predictors of attendance in a series of regression models. Of the 39 percent of families that attended any program sessions, the majority (71 percent) attended at least four of the seven sessions. Attendance varied significantly across the geographic areas in which groups were held. Prior service utilization, youth conduct problem behavior, parental history of deployment, and family conflict were each positively associated with attendance, whereas parent tobacco use was negatively associated with attendance. These results highlight the challenges in recruiting military families into in-person prevention programs and suggest that extra efforts may be needed to engage families that do not perceive that they have a need for support.
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Mwebe, Robert, Chester Kalinda, Ekwaro A. Obuku, Eve Namisango, Alison A. Kinengyere, Moses Ocan, Ann Nanteza, Savino Biryomumaisho, and Lawrence Mugisha. Epidemiology and effectiveness of interventions for Foot and Mouth Disease in Africa: A protocol for systematic review and meta-analysis. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, November 2022. http://dx.doi.org/10.37766/inplasy2022.11.0039.

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Review question / Objective: What is the epidemiology and effectiveness of control measures for foot and mouth disease in African countries?’ PICOS: Description of elements Population/ problem/Setting: Artiodactyla (cloven ungulates), domestic (cattle, sheep, goats, and pigs), camels and wildlife (buffaloes, deer, antelope, wild pigs, elephant, giraffe, and camelids) affected by Foot and Mouth Disease (FMD) or Hoof and Mouth Disease (HMD) caused by the Foot and Mouth Disease Virus (FMDV) in Africa. Intervention: Prevention measures: vaccination, ‘biosafety and biosecurity’, sensitization of the public. Control measures: quarantine, movement control, closure of markets and stock routes, mouth swabbing of animals with infected materials (old technique that is no long applicable), culling, mass slaughter, stamping out and any other interventions or control measures generally accepted by the ‘community of practice’ of animal health practitioners. Comparator: areas that did not have any control activities for FMD, in head-to-head comparisons in the same study. Outcome: epidemiological outcomes: incidence, prevalence, patterns or trends, clinical symptoms, and risk factors. Effectiveness outcomes: success, and usefulness of the interventions measured as averted deaths, illness and infections, and costs associated with the interventions (cost–effectiveness). Study design: epidemiological designs include cohort design for incidence, cross sectional for prevalence and case-control for clinical symptoms and risk factors. Interventional designs include randomized controlled trials, cluster randomized trials, quasi-experimental designs – controlled before and after, interrupted time series, [regression discontinuity design, difference-in-difference, and propensity score matching]. Timelines: 1900 – 2022.
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Kim, Changmo, Ghazan Khan, Brent Nguyen, and Emily L. Hoang. Development of a Statistical Model to Predict Materials’ Unit Prices for Future Maintenance and Rehabilitation in Highway Life Cycle Cost Analysis. Mineta Transportation Institute, December 2020. http://dx.doi.org/10.31979/mti.2020.1806.

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The main objectives of this study are to investigate the trends in primary pavement materials’ unit price over time and to develop statistical models and guidelines for using predictive unit prices of pavement materials instead of uniform unit prices in life cycle cost analysis (LCCA) for future maintenance and rehabilitation (M&R) projects. Various socio-economic data were collected for the past 20 years (1997–2018) in California, including oil price, population, government expenditure in transportation, vehicle registration, and other key variables, in order to identify factors affecting pavement materials’ unit price. Additionally, the unit price records of the popular pavement materials were categorized by project size (small, medium, large, and extra-large). The critical variables were chosen after identifying their correlations, and the future values of each variable were predicted through time-series analysis. Multiple regression models using selected socio-economic variables were developed to predict the future values of pavement materials’ unit price. A case study was used to compare the results between the uniform unit prices in the current LCCA procedures and the unit prices predicted in this study. In LCCA, long-term prediction involves uncertainties due to unexpected economic trends and industrial demand and supply conditions. Economic recessions and a global pandemic are examples of unexpected events which can have a significant influence on variations in material unit prices and project costs. Nevertheless, the data-driven scientific approach as described in this research reduces risk caused by such uncertainties and enables reasonable predictions for the future. The statistical models developed to predict the future unit prices of the pavement materials through this research can be implemented to enhance the current LCCA procedure and predict more realistic unit prices and project costs for the future M&R activities, thus promoting the most cost-effective alternative in LCCA.
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