Relevant bibliographies by topics / Mixed variables

Academic literature on the topic 'Mixed variables'

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Published: 1 June 2024

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Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Journal articles on the topic "Mixed variables":

1

Daudin, J. J. "Selection of Variables in Mixed-Variable Discriminant Analysis." Biometrics 42, no. 3 (September 1986): 473. http://dx.doi.org/10.2307/2531198.

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ARAKAWA, Masao, Takaharu Shirai, Hitomi Kono, Hirotaka NAKAYAMA, and Hiroshi ISHIKAWA. "Approximate Optimization Using RBF : Mixed variable Optimization with Discrete Variables." Proceedings of Design & Systems Conference 2003.13 (2003): 108–11. http://dx.doi.org/10.1299/jsmedsd.2003.13.108.

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Vagabov, A. I., and A. H. Abud. "Variable separation method in solving multidimensional mixed problems with separable variables." Doklady Mathematics 89, no. 3 (May 2014): 263–66. http://dx.doi.org/10.1134/s1064562414030053.

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Galicer, Daniel, Martín Mansilla, and Santiago Muro. "Mixed Bohr radius in several variables." Transactions of the American Mathematical Society 373, no. 2 (November 5, 2019): 777–96. http://dx.doi.org/10.1090/tran/7870.

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Saracco, J., and M. Chavent. "Clustering of Variables for Mixed Data." EAS Publications Series 77 (2016): 121–69. http://dx.doi.org/10.1051/eas/1677007.

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Andrews, Bryan, Joseph Ramsey, and Gregory F. Cooper. "Scoring Bayesian networks of mixed variables." International Journal of Data Science and Analytics 6, no. 1 (January 11, 2018): 3–18. http://dx.doi.org/10.1007/s41060-017-0085-7.

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Hamid, Hashibah, Nor Idayu Mahat, and Safwati Ibrahim. "ADAPTIVE VARIABLE EXTRACTIONS WITH LDA FOR CLASSIFICATION OF MIXED VARIABLES, AND APPLICATIONS TO MEDICAL DATA." Journal of Information and Communication Technology 20, Number 3 (June 11, 2021): 305–27. http://dx.doi.org/10.32890/jict2021.20.3.2.

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The strategy surrounding the extraction of a number of mixed variables is examined in this paper in building a model for Linear Discriminant Analysis (LDA). Two methods for extracting crucial variables from a dataset with categorical and continuous variables were employed, namely, multiple correspondence analysis (MCA) and principal component analysis (PCA). However, in this case, direct use of either MCA or PCA on mixed variables is impossible due to restrictions on the structure of data that each method could handle. Therefore, this paper executes some adjustments including a strategy for managing mixed variables so that those mixed variables are equivalent in values. With this, both MCA and PCA can be performed on mixed variables simultaneously. The variables following this strategy of extraction were then utilised in the construction of the LDA model before applying them to classify objects going forward. The suggested models, using three real sets of medical data were then tested, where the results indicated that using a combination of the two methods of MCA and PCA for extraction and LDA could reduce the model’s size, having a positive effect on classifying and better performance of the model since it leads towards minimising the leave-one-out error rate. Accordingly, the models proposed in this paper, including the strategy that was adapted was successful in presenting good results over the full LDA model. Regarding the indicators that were used to extract and to retain the variables in the model, cumulative variance explained (CVE), eigenvalue, and a non-significant shift in the CVE (constant change), could be considered a useful reference or guideline for practitioners experiencing similar issues in future.
8

Lijie, Cui, Lü Zhenzhou, and Li Guijie. "Reliability Analysis in Presence of Random Variables and Fuzzy Variables." Journal of Applied Mathematics 2015 (2015): 1–8. http://dx.doi.org/10.1155/2015/365051.

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For mixed uncertainties of random variables and fuzzy variables in engineering, three indices, that is, interval reliability index, mean reliability index, and numerical reliability index, are proposed to measure safety of structure. Comparing to the reliability membership function for measuring the safety in case of mixed uncertainties, the proposed indices are more intuitive and easier to represent the safety degree of the engineering structure, and they are more suitable for the reliability design in the case of the mixed uncertainties. The differences and relations among three proposed indices are investigated, and their applicability is compared. Furthermore, a technique based on the probability density function evolution method is employed to improve the computational efficiency of the proposed indices. At last, a numerical example and two engineering examples are illustrated to demonstrate the feasibility, reasonability, and efficiency of the computational technique of the proposed indices.
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Lee, Min Ho. "Mixed Jacobi-like forms of several variables." International Journal of Mathematics and Mathematical Sciences 2006 (2006): 1–14. http://dx.doi.org/10.1155/ijmms/2006/31542.

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We study mixed Jacobi-like forms of several variables associated to equivariant maps of the Poincaré upper half-plane in connection with usual Jacobi-like forms, Hilbert modular forms, and mixed automorphic forms. We also construct a lifting of a mixed automorphic form to such a mixed Jacobi-like form.
10

Shim, Jooyong. "Kernel Poisson regression for mixed input variables." Journal of the Korean Data and Information Science Society 23, no. 6 (November 30, 2012): 1231–39. http://dx.doi.org/10.7465/jkdi.2012.23.6.1231.

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You might also be interested in the extended bibliographies on the topic 'Mixed variables' for particular source types:
Journal articles Dissertations / Theses Books

Dissertations / Theses on the topic "Mixed variables":

1

Moustaki, Irini. "Latent variable models for mixed manifest variables." Thesis, London School of Economics and Political Science (University of London), 1996. http://etheses.lse.ac.uk/78/.

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Latent variable models are widely used in social sciences in which interest is centred on entities such as attitudes, beliefs or abilities for which there e)dst no direct measuring instruments. Latent modelling tries to extract these entities, here described as latent (unobserved) variables, from measurements on related manifest (observed) variables. Methodology already exists for fitting a latent variable model to manifest data that is either categorical (latent trait and latent class analysis) or continuous (factor analysis and latent profile analysis). In this thesis a latent trait and a latent class model are presented for analysing the relationships among a set of mixed manifest variables using one or more latent variables. The set of manifest variables contains metric (continuous or discrete) and binary items. The latent dimension is continuous for the latent trait model and discrete for the latent class model. Scoring methods for allocating individuals on the identified latent dimen-sions based on their responses to the mixed manifest variables are discussed. ' Item nonresponse is also discussed in attitude scales with a mixture of binary and metric variables using the latent trait model. The estimation and the scoring methods for the latent trait model have been generalized for conditional distributions of the observed variables given the vector of latent variables other than the normal and the Bernoulli in the exponential family. To illustrate the use of the naixed model four data sets have been analyzed. Two of the data sets contain five memory questions, the first on Thatcher's resignation and the second on the Hillsborough football disaster; these five questions were included in BMRBI's August 1993 face to face omnibus survey. The third and the fourth data sets are from the 1990 and 1991 British Social Attitudes surveys; the questions which have been analyzed are from the sexual attitudes sections and the environment section respectively.
2

Chang, Soong Uk. "Clustering with mixed variables /." [St. Lucia, Qld.], 2005. http://www.library.uq.edu.au/pdfserve.php?image=thesisabs/absthe19086.pdf.

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Mahat, Nor Idayu. "Some investigations in discriminant analysis with mixed variables." Thesis, University of Exeter, 2006. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.432783.

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The location model is a potential basis for discriminating between groups of objects with mixed types of variables. The model specifies a parametric form for the conditional distribution of the continuous variables given each pattern of values of the categorical variables, thus leading to a theoretical discriminant function between the groups. To conduct a practical discriminant analysis, the objects must first be sorted into the cells of a multinomial table generated from the categorical values, and the model parameters must then be estimated from the data. However, in many practical situations some of the cells are empty, which prevents simple implementation of maximum likelihood estimation and restricts the feasibility of linear model estimators to cases with relatively few categorical variables. This deficiency was overcome by non-parametric smoothing estimation proposed by Asparoukhov and Krzanowski (2000). Its usual implementation uses exponential and piece-wise smoothing functions for the continuous variables, and adaptive weighted nearest neighbour for the categorical variables. Despite increasing the range of applicability, the smoothing parameters that are chosen by maximising the leave-one-out pseudo-likelihood depend on distributional assumptions, while, the smoothing method for the categorical variables produces erratic values if the number of variables is large. This thesis rectifies these shortcomings, and extends location model methodology to situations where there are large numbers of mixed categorical and continuous variables. Chapter 2 uses the simplest form of the exponential smoothing function for the continuous variables and describes how the smoothing parameters can instead be chosen by minimising either the leave-one-out error rate or the leave-one-out Brier score, neither of which make distributional assumptions. Alternative smoothing methods, namely a kernel and a weighted form of the maximum likelihood, are also investigated for the categorical variables. Numerical evidence in Chapter 3 shows that there is little to choose among the strategies for estimating smoothing parameters and among the smoothing methods for the categorical variables. However, some of the proposed smoothing methods are more feasible when the number of parameters to be estimated is reduced. Chapter 4 reviews previous work on problems of high dimensional feature variables, and focuses on selecting variables on the basis of the distance between groups. In particular, the Kullback-Leibler divergence is considered for the location model, but existing theory based on maximum likelihood estimators is not applicable for general cases. Chapter 5 therefore describes the implementation of this distance for smoothed estimators, and investigates its asymptotic distribution. The estimated distance and its asymptotic distribution provide a stopping rule in a sequence of searching processes, either by forward, backward or stepwise selections, following the test for no additional information. Simulation results in Chapter 6 exhibit the feasibility of the proposed variable selection strategies for large numbers of variables, but limitations in several circumstances are identified. Applications to real data sets in Chapter 7 show how the proposed methods are competitive with, and sometimes better than other existing classification methods. Possible future work is outlined in Chapter 8.
4

Pelamatti, Julien. "Mixed-variable Bayesian optimization : application to aerospace system design." Thesis, Lille 1, 2020. http://www.theses.fr/2020LIL1I003.

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Dans le cadre de la conception de systèmes complexes, tels que les aéronefs et les lanceurs, la présence de fonctions d'objectifs et/ou de contraintes à forte intensité de calcul (e.g., modèles d'éléments finis) couplée à la dépendance de choix de conception technologique discrets et non ordonnés entraîne des problèmes d'optimisation difficiles. De plus, une partie de ces choix technologiques est associée à un certain nombre de variables de conception continues et discrètes spécifiques qui ne doivent être prises en considération que si des choix technologiques spécifiques sont faits. Par conséquent, le problème d'optimisation qui doit être résolu afin de déterminer la conception optimale du système présente un espace de recherche et un domaine de faisabilité variant de façon dynamique. Les algorithmes existants qui permettent de résoudre ce type particulier de problèmes ont tendance à exiger une grande quantité d'évaluations de fonctions afin de converger vers l'optimum réalisable, et sont donc inadéquats lorsqu'il s'agit de résoudre les problèmes à forte intensité de calcul. Pour cette raison, cette thèse explore la possibilité d'effectuer une optimisation de l'espace de conception contraint à variables mixtes et de taille variable en s'appuyant sur des méthodes d’optimisation à base de modèles de substitution créés à l'aide de processus Gaussiens, également connue sous le nom d'optimisation Bayésienne. Plus spécifiquement, 3 axes principaux sont discutés. Premièrement, la modélisation de substitution par processus gaussien de fonctions mixtes continues/discrètes et les défis qui y sont associés sont discutés en détail. Un formalisme unificateur est proposé afin de faciliter la description et la comparaison entre les noyaux existants permettant d'adapter les processus gaussiens à la présence de variables discrètes non ordonnées. De plus, les performances réelles de modélisation de ces différents noyaux sont testées et comparées sur un ensemble de benchmarks analytiques et de conception ayant des caractéristiques et des paramétrages différents. Dans la deuxième partie de la thèse, la possibilité d'étendre la modélisation de substitution mixte continue/discrète à un contexte d'optimisation Bayésienne est discutée. La faisabilité théorique de cette extension en termes de modélisation de la fonction objectif/contrainte ainsi que de définition et d'optimisation de la fonction d'acquisition est démontrée. Différentes alternatives possibles sont considérées et décrites. Enfin, la performance de l'algorithme d'optimisation proposé, avec diverses paramétrisations des noyaux et différentes initialisations, est testée sur un certain nombre de cas-test analytiques et de conception et est comparée aux algorithmes de référence.Dans la dernière partie de ce manuscrit, deux approches permettant d'adapter les algorithmes d'optimisation bayésienne mixte continue/discrète discutés précédemment afin de résoudre des problèmes caractérisés par un espace de conception variant dynamiquement au cours de l’optimisation sont proposées. La première adaptation est basée sur l'optimisation parallèle de plusieurs sous-problèmes couplée à une allocation de budget de calcul basée sur l'information fournie par les modèles de substitution. La seconde adaptation, au contraire, est basée sur la définition d'un noyau permettant de calculer la covariance entre des échantillons appartenant à des espaces de recherche partiellement différents en fonction du regroupement hiérarchique des variables dimensionnelles. Enfin, les deux alternatives sont testées et comparées sur un ensemble de cas-test analytiques et de conception.Globalement, il est démontré que les méthodes d'optimisation proposées permettent de converger vers les optimums des différents types de problèmes considérablement plus rapidement par rapport aux méthodes existantes. Elles représentent donc un outil prometteur pour la conception de systèmes complexes
Within the framework of complex system design, such as aircraft and launch vehicles, the presence of computationallyintensive objective and/or constraint functions (e.g., finite element models and multidisciplinary analyses)coupled with the dependence on discrete and unordered technological design choices results in challenging optimizationproblems. Furthermore, part of these technological choices is associated to a number of specific continuous anddiscrete design variables which must be taken into consideration only if specific technological and/or architecturalchoices are made. As a result, the optimization problem which must be solved in order to determine the optimalsystem design presents a dynamically varying search space and feasibility domain.The few existing algorithms which allow solving this particular type of problems tend to require a large amountof function evaluations in order to converge to the feasible optimum, and result therefore inadequate when dealingwith the computationally intensive problems which can often be encountered within the design of complex systems.For this reason, this thesis explores the possibility of performing constrained mixed-variable and variable-size designspace optimization by relying on surrogate model-based design optimization performed with the help of Gaussianprocesses, also known as Bayesian optimization. More specifically, 3 main axes are discussed. First, the Gaussianprocess surrogate modeling of mixed continuous/discrete functions and the associated challenges are extensivelydiscussed. A unifying formalism is proposed in order to facilitate the description and comparison between theexisting kernels allowing to adapt Gaussian processes to the presence of discrete unordered variables. Furthermore,the actual modeling performances of these various kernels are tested and compared on a set of analytical and designrelated benchmarks with different characteristics and parameterizations.In the second part of the thesis, the possibility of extending the mixed continuous/discrete surrogate modeling toa context of Bayesian optimization is discussed. The theoretical feasibility of said extension in terms of objective/-constraint function modeling as well as acquisition function definition and optimization is shown. Different possiblealternatives are considered and described. Finally, the performance of the proposed optimization algorithm, withvarious kernels parameterizations and different initializations, is tested on a number of analytical and design relatedtest-cases and compared to reference algorithms.In the last part of this manuscript, two alternative ways of adapting the previously discussed mixed continuous/discrete Bayesian optimization algorithms in order to solve variable-size design space problems (i.e., problemscharacterized by a dynamically varying design space) are proposed. The first adaptation is based on the paralleloptimization of several sub-problems coupled with a computational budget allocation based on the informationprovided by the surrogate models. The second adaptation, instead, is based on the definition of a kernel allowingto compute the covariance between samples belonging to partially different search spaces based on the hierarchicalgrouping of design variables. Finally, the two alternatives are tested and compared on a set of analytical and designrelated benchmarks.Overall, it is shown that the proposed optimization methods allow to converge to the various constrained problemoptimum neighborhoods considerably faster when compared to the reference methods, thus representing apromising tool for the design of complex systems
5

Lazare, Arnaud. "Global optimization of polynomial programs with mixed-integer variables." Thesis, Université Paris-Saclay (ComUE), 2019. http://www.theses.fr/2019SACLY011.

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Dans cette thèse, nous nous intéressons à l'étude des programmes polynomiaux, c'est à dire les problème d'optimisation dont la fonction objectif et/ou les contraintes font intervenir des polynômes de plusieurs variables. Ces problèmes ont de nombreuses applications pratiques et constituent actuellement un champ de recherche très actif. Différentes méthodes permettent de les résoudre de façon exacte ou approchée, en utilisant par exemple des relaxationssemidéfinies positives du type "moments-somme de carrés". Mais ces problèmes restent très difficiles et on ne sait résoudre en toute généralité que des instances de petite taille.Dans le cas quadratique, une approche de résolution exacte efficace a été initialement proposée à travers la méthode QCR. Elle se base sur une reformulation quadratique convexe "optimale" au sens de la borne par relaxation continue.Une des motivations de cette thèse est de généraliser cette approche pour le cas des problèmes polynomiaux. Dans la majeure partie de ce manuscrit, nous étudions les problèmes d'optimisation en variables binaires. Nous proposons deux familles de reformulations convexes pour ces problèmes: des reformulations "directes" et des reformulations passant par la quadratisation.Pour les reformulations directes, nous nous intéressons tout d'abord aux linéarisations. Nous introduisons le concept de q-linéarisation, une linéarisation utilisant q variables additionnelles, et comparons les bornes obtenues par relaxation continue pour différentes valeurs de q. Ensuite, nous appliquons la reformulation convexe au problème polynomial, en ajoutant des termes supplémentaires à la fonction objectif, mais sans ajouter de variables ou de contraintes additionnelles.La deuxième famille de reformulations convexes vise à étendre la reformulation quadratique convexe au cas polynomial. Nous proposons plusieurs nouvelles reformulations alternatives que nous comparons aux méthodes existantes sur des instances de la littérature. En particulier nous présentons l'algorithme PQCR pour résoudre des problèmes polynomiaux binaires sans contrainte. La méthode PQCR permet de résoudre des instances jusqu'ici non résolues. En plus des expérimentations numériques, nous proposons aussi une étude théorique visant à comparer les différentes reformulations quadratiques de la littérature puis à leur appliquer une reformulation convexe.Enfin nous considérons des cas plus généraux et nous proposons une méthode permettant de calculer des relaxations convexes pour des problèmes continus
In this thesis, we are interested in the study of polynomial programs, that is optimization problems for which the objective function and/or the constraints are expressed by multivariate polynomials. These problems have many practical applications and are currently actively studied. Different methods can be used to find either a global or a heuristic solution, using for instance, positive semi-definite relaxations as in the "Moment/Sum of squares" method. But these problems remain very difficult and only small instances are addressed. In the quadratic case, an effective exact solution approach was initially proposed in the QCR method. It is based on a quadratic convex reformulation, which is optimal in terms of continuous relaxation bound.One of the motivations of this thesis is to generalize this approach to the case of polynomial programs. In most of this manuscript, we study optimization problems with binary variables. We propose two families of convex reformulations for these problems: "direct" reformulations and quadratic ones.For direct reformulations, we first focus on linearizations. We introduce the concept of q-linearization, that is a linearization using q additional variables, and we compare the bounds obtained by continuous relaxation for different values of q. Then, we apply convex reformulation to the polynomial problem, by adding additional terms to the objective function, but without adding additional variables or constraints.The second family of convex reformulations aims at extending quadratic convex reformulation to the polynomial case. We propose several new alternative reformulations that we compare to existing methods on instances of the literature. In particular we present the algorithm PQCR to solve unconstrained binary polynomial problems. The PQCR method is able to solve several unsolved instances. In addition to numerical experiments, we also propose a theoretical study to compare the different quadratic reformulations of the literature and then apply a convex reformulation to them.Finally, we consider more general problems and we propose a method to compute convex relaxations for continuous problems
6

Bonnet, Anna. "Heritability Estimation in High-dimensional Mixed Models : Theory and Applications." Thesis, Université Paris-Saclay (ComUE), 2016. http://www.theses.fr/2016SACLS498/document.

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Nous nous intéressons à desméthodes statistiques pour estimer l'héritabilitéd'un caractère biologique, qui correspond à lapart des variations de ce caractère qui peut êtreattribuée à des facteurs génétiques. Nousproposons dans un premier temps d'étudierl'héritabilité de traits biologiques continus àl'aide de modèles linéaires mixtes parcimonieuxen grande dimension. Nous avons recherché lespropriétés théoriques de l'estimateur du maximumde vraisemblance de l'héritabilité : nousavons montré que cet estimateur était consistantet vérifiait un théorème central limite avec unevariance asymptotique que nous avons calculéeexplicitement. Ce résultat, appuyé par des simulationsnumériques sur des échantillons finis,nous a permis de constater que la variance denotre estimateur était très fortement influencéepar le ratio entre le nombre d'observations et lataille des effets génétiques. Plus précisément,quand le nombre d’observations est faiblecomparé à la taille des effets génétiques (ce quiest très souvent le cas dans les étudesgénétiques), la variance de l’estimateur était trèsgrande. Ce constat a motivé le développementd'une méthode de sélection de variables afin dene garder que les variants génétiques les plusimpliqués dans les variations phénotypiques etd’améliorer la précision des estimations del’héritabilité.La dernière partie de cette thèse est consacrée àl'estimation d'héritabilité de données binaires,dans le but d'étudier la part de facteursgénétiques impliqués dans des maladies complexes.Nous proposons d'étudier les propriétésthéoriques de la méthode développée par Golanet al. (2014) pour des données de cas-contrôleset très efficace en pratique. Nous montronsnotamment la consistance de l’estimateur del’héritabilité proposé par Golan et al. (2014)
We study statistical methods toestimate the heritability of a biological trait,which is the proportion of variations of thistrait that can be explained by genetic factors.First, we propose to study the heritability ofquantitative traits using high-dimensionalsparse linear mixed models. We investigate thetheoretical properties of the maximumlikelihood estimator for the heritability and weshow that it is a consistent estimator and that itsatisfies a central limit theorem with a closedformexpression for the asymptotic variance.This result, supported by an extendednumerical study, shows that the variance of ourestimator is strongly affected by the ratiobetween the number of observations and thesize of the random genetic effects. Moreprecisely, when the number of observations issmall compared to the size of the geneticeffects (which is often the case in geneticstudies), the variance of our estimator is verylarge. This motivated the development of avariable selection method in order to capturethe genetic variants which are involved themost in the phenotypic variations and providemore accurate heritability estimations. Wepropose then a variable selection methodadapted to high dimensional settings and weshow that, depending on the number of geneticvariants actually involved in the phenotypicvariations, called causal variants, it was a goodidea to include or not a variable selection stepbefore estimating heritability.The last part of this thesis is dedicated toheritability estimation for binary data, in orderto study the proportion of genetic factorsinvolved in complex diseases. We propose tostudy the theoretical properties of the methoddeveloped by Golan et al. (2014) for casecontroldata, which is very efficient in practice.Our main result is the proof of the consistencyof their heritability estimator
7

Adamec, Vaclav. "The Effect of Maternal and Fetal Inbreeding on Dystocia, Calf Survival, Days to First Service and Non-Return Performance in U.S. Dairy Cattle." Diss., Virginia Tech, 2002. http://hdl.handle.net/10919/25999.

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Intensive selection for increased milk production over many generations has led to growing genetic similarity and increased relationships in dairy population. In the current study, inbreeding depression was estimated for number of days to first service, summit milk, conception by 70 days non-return, and calving rate with a linear mixed model (LMM) approach and for calving difficulty, calf mortality with a Bayesian threshold model (BTM) for categorical traits. Effectiveness of classical and unknown parentage group procedures to estimate inbreeding coefficients was evaluated depending on completeness of a 5-generation pedigree. A novel method derived from the classical formula to estimate inbreeding was utilized to evaluate completeness of pedigrees. Two different estimates of maternal inbreeding were fitted in separate models as a linear covariate in combined LMM analyses (Holstein registered and grade cows and Jersey cows) or separate analyses (registered Holstein cows) by parity (1-4) with fetal inbreeding. Impact of inbreeding type, model, data structure, and treatment of herd-year-season (HYS) on magnitude and size of inbreeding depression were assessed. Grade Holstein datasets were sampled and analyzed by percentage of pedigree present (0-30%, 30-70% and 70-100%). BTM analyses (sire-mgs) were performed using Gibbs sampling for parities 1, 2 and 3 fitting maternal inbreeding only. In LMM analyses of grade data, the least pedigree and diagonal A matrix performed the worst. Significant inbreeding effects were obtained in most traits in cows of parity 1. Fetal inbreeding depression was mostly lower than that from maternal inbreeding. Inbreeding depression in binary traits was the most difficult to evaluate. Analyses with non-additive effects included in LMM, for data by inbreeding level and by age group should be preferred to estimate inbreeding depression. In BTM inbreeding effects were strongly related to dam parity and calf sex. Largest effects were obtained from parity 1 cows giving birth to male calves (0.417% and 0.252% for dystocia and calf mortality) and then births to female calves (0.300% and 0.203% for dystocia and calf mortality). Female calves from mature cows were the least affected (0.131% and 0.005% for dystocia and calf mortality). Data structure was found to be a very important factor to attainment of convergence in distribution.
Ph. D.
8

Fernández, Villegas Renzo. "A beta inflated mean regression model with mixed effects for fractional response variables." Master's thesis, Pontificia Universidad Católica del Perú, 2017. http://tesis.pucp.edu.pe/repositorio/handle/123456789/8847.

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In this article we propose a new mixed effects regression model for fractional bounded response variables. Our model allows us to incorporate covariates directly to the expected value, so we can quantify exactly the influence of these covariates in the mean of the variable of interest rather than to the conditional mean. Estimation is carried out from a Bayesian perspective and due to the complexity of the augmented posterior distribution we use a Hamiltonian Monte Carlo algorithm, the No-U-Turn sampler, implemented using Stan software. A simulation study for comparison, in terms of bias and RMSE, was performed showing that our model has a better performance than other traditional longitudinal models for bounded variables. Finally, we applied our Beta Inflated mixed-effects regression model to real data which consists of utilization of credit lines in the peruvian financial system.
En este artículo proponemos un nuevo modelo de regresión con efectos mixtos para variables acotadas fraccionarias. Este modelo nos permite incorporar covariables directamente al valor esperado, de manera que podemos cuantificar exactamente la influencia de estas covariables en la media de la variable de interés en vez de en la media condicional. La estimación se llevó a cabo desde una perspectiva bayesiana y debido a la complejidad de la distribución aumentada a posteriori usamos un algoritmo de Monte Carlo Hamiltoniano, el muestreador No-U-Turn, que se encuentra implementado en el software Stan. Se realizó un estudio de simulación que compara, en términos de sesgo y RMSE, el modelo propuesto con otros modelos tradicionales longitudinales para variables acotadas, resultando que el primero tiene un mejor desempeño. Finalmente, aplicamos nuestro modelo de regresión Beta Inflacionada con efectos mixtos a datos reales los cuales consistían en información de la utilización de las líneas de crédito en el sistema financiero peruano.
Tesis
9

Dahito, Marie-Ange. "Constrained mixed-variable blackbox optimization with applications in the automotive industry." Electronic Thesis or Diss., Institut polytechnique de Paris, 2022. http://www.theses.fr/2022IPPAS017.

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Bon nombre de problèmes d'optimisation rencontrés dans l'industrie font appel à des systèmes complexes et n'ont pas de formulation analytique explicite : ce sont des problèmes d'optimisation de type boîte noire (ou blackbox en anglais). Ils peuvent être dits “mixtes”, auquel cas ils impliquent des variables de différentes natures (continues et discrètes), et avoir de nombreuses contraintes à satisfaire. De plus, les évaluations de l'objectif et des contraintes peuvent être numériquement coûteuses.Dans cette thèse, nous étudions des méthodes de résolution de tels problèmes complexes, à savoir des problèmes d'optimisation boîte noire avec contraintes et variables mixtes, pour lesquels les évaluations des fonctions sont très coûteuses en temps de calcul.Puisque l'utilisation de dérivées n'est pas envisageable, ce type de problèmes est généralement abordé par des approches sans dérivées comme les algorithmes évolutionnaires, les méthodes de recherche directe et les approches basées sur des métamodèles.Nous étudions les performances de telles méthodes déterministes et stochastiques dans le cadre de l'optimisation boîte noire, y compris sur un cas test en éléments finis que nous avons conçu. En particulier, nous évaluons les performances de la variante ORTHOMADS de l'algorithme de recherche directe MADS sur des problèmes d'optimisation continus et à variables mixtes issus de la littérature.Nous proposons également une nouvelle méthode d'optimisation boîte noire, nommée BOA, basée sur des approximations par métamodèles. Elle comporte deux phases dont la première vise à trouver un point réalisable tandis que la seconde améliore itérativement la valeur de l'objectif de la meilleure solution réalisable trouvée. Nous décrivons des expériences utilisant des instances de la littérature et des applications de l'industrie automobile. Elles incluent des tests de notre algorithme avec différents types de métamodèles, ainsi que des comparaisons avec ORTHOMADS
Numerous industrial optimization problems are concerned with complex systems and have no explicit analytical formulation, that is they are blackbox optimization problems. They may be mixed, namely involve different types of variables (continuous and discrete), and comprise many constraints that must be satisfied. In addition, the objective and constraint blackbox functions may be computationally expensive to evaluate.In this thesis, we investigate solution methods for such challenging problems, i.e constrained mixed-variable blackbox optimization problems involving computationally expensive functions.As the use of derivatives is impractical, problems of this form are commonly tackled using derivative-free approaches such as evolutionary algorithms, direct search and surrogate-based methods.We investigate the performance of such deterministic and stochastic methods in the context of blackbox optimization, including a finite element test case designed for our research purposes. In particular, the performance of the ORTHOMADS instantiation of the direct search MADS algorithm is analyzed on continuous and mixed-integer optimization problems from the literature.We also propose a new blackbox optimization algorithm, called BOA, based on surrogate approximations. It proceeds in two phases, the first of which focuses on finding a feasible solution, while the second one iteratively improves the objective value of the best feasible solution found. Experiments on instances stemming from the literature and applications from the automotive industry are reported. They namely include results of our algorithm considering different types of surrogates and comparisons with ORTHOMADS
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Mohd, Isa Khadijah. "Corporate taxpayers’ compliance variables under the self-assessment system in Malaysia : a mixed methods approach." Thesis, Curtin University, 2012. http://hdl.handle.net/20.500.11937/1796.

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This thesis examines corporate taxpayers’ compliance variables and analyses the influence of business characteristics on compliance behaviour. A two-phase exploratory mixed methods approach was employed to explore participants’ views of corporate taxpayers’ compliance variables, with the intention of using this information to develop a survey instrument. The method comprised eight focus group interviews with 60 tax auditors from the Inland Revenue Board of Malaysia (IRBM), and a mixed-mode survey among selected Malaysian corporate taxpayers. Thematic analysis and descriptive and inferential analysis were mainly used to examine the qualitative and quantitative data.The results suggest that the main corporate taxpayers’ compliance variables are: tax knowledge, tax complexity, tax agents and tax audits. The main business characteristics that are found to have significant influence on compliance variables are the length of time the business has been operational, size and industry. Continuous tax education and tax audit programmes are thus vital, and should focus more closely on specific groups of taxpayers, namely smaller and more newly established companies, companies in rural areas, and business industries that are more inclined to use cash transactions. Moreover, as many corporate taxpayers perceive the probability of an audit as low, the IRBM should publicise its audit activities more prolifically through available media channels. Tax simplification, especially on laws regarding estimation of income tax, is also an important consideration.This study extends the scope of tax compliance research to corporate taxpayers, and builds upon the limited international and Malaysian literature in this area. Most of the research findings of this thesis yield consistent results with respect to particular tax compliance variables. In a tax policy context, this study enables international tax authorities in general, and Malaysian tax authorities in particular, to have greater confidence in developing and administering tax laws and policies to maintain and/or increase the overall level of corporate compliance.
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Books on the topic "Mixed variables":

1

Barnes, William John. Complex variables: Poems & music. Kingston, Ont: Quarry Press, 1994.

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Jiang, Jiming. Robust Mixed Model Analysis. Singapore: World Scientific Publishing Co Pte Ltd, 2019.

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Fisher, S. A. Internal performance of a variable ramp mixed compression intake at Mach 3.05. Melbourne, Vic: Aeronautical Research Laboratories, 1985.

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1937-, Didion D. A., and Air and Energy Engineering Research Laboratory, eds. A performance evaluation of a variable speed, mixed refrigerant heat pump: Project summary. Research Triangle Park, NC: U.S. Environmental Protection Agency, Air and Energy Engineering Research Laboratory, 1992.

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Mikielewicz, Dariusz Przemysław. Comparative studies of turbulence models under conditions of mixed convectionwith variable properties in heated vertical tubes. Manchester: University of Manchester, 1994.

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Alves, Trevor Darren. The performance of simple bioprocess models for state variable tracking and change detection in well-mixed bioreactors. Birmingham: University of Birmingham, 1997.

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Nikolakakis, Thomas. A Mixed Integer Linear Unit Commitment and Economic Dispatch Model for Thermo-Electric and Variable Renewable Energy Generators With Compressed Air Energy Storage. [New York, N.Y.?]: [publisher not identified], 2017.

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Gatica, Gabriel N. Simple Introduction to the Mixed Finite Element Method: Theory and Applications. Springer London, Limited, 2014.

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A Simple Introduction To The Mixed Finite Element Method Theory And Applications. Springer International Publishing AG, 2014.

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A Beginner's Guide to Generalized Additive Mixed Models with R. New York, USA: Highland Statistics Ltd, 2014.

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Book chapters on the topic "Mixed variables":

1

Berry, Kenneth J., Janis E. Johnston, and Paul W. Mielke. "Mixed-Level Variables." In The Measurement of Association, 439–510. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-98926-6_8.

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Cartwright, Elizabeth, and Jerome Crowder. "Creating Visual Variables." In The Handbook of Teaching Qualitative and Mixed Research Methods, 215–18. London: Routledge, 2023. http://dx.doi.org/10.4324/9781003213277-53.

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Salinas Ruíz, Josafhat, Osval Antonio Montesinos López, Gabriela Hernández Ramírez, and Jose Crossa Hiriart. "Generalized Linear Models." In Generalized Linear Mixed Models with Applications in Agriculture and Biology, 43–84. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-32800-8_2.

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AbstractIn the generalized linear model (GLM) (which is not highly general) y = Xβ + ϵ, the response variables are normally distributed, with constant variance across the values of all the predictor variables, and are linear functions of the predictor variables. Transformations of data are used to try to force the data into a normal linear regression model or to find a non-normal-type response variable transformation (discrete, categorical, positive continuous scale, etc.) that is linearly related to the predictor variables; however, this is no longer necessary. Instead of using a normal distribution, a positively skewed distribution with values that are positive real numbers can be selected. Generalized linear models (GLMs) go beyond linear mixed models, taking into account that the response variables are not of continuous scale (not normally distributed), GLMs are heteroscedastic, and there is a linear relationship between the mean of the response variable and the predictor or explanatory variables.
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Dunn, John C., and Michael L. Kalish. "Mixed Designs with Continuous Dependent Variables." In State-Trace Analysis, 57–72. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-73129-2_5.

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Laghi, Annalisa, and Laura Lizzani. "Projection Pursuit Regression with Mixed Variables." In Studies in Classification, Data Analysis, and Knowledge Organization, 303–10. Berlin, Heidelberg: Springer Berlin Heidelberg, 1999. http://dx.doi.org/10.1007/978-3-642-60126-2_38.

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Zografos, Konstantinos. "Entropy and Divergence Measures for Mixed Variables." In Statistical Models and Methods for Biomedical and Technical Systems, 519–34. Boston, MA: Birkhäuser Boston, 2008. http://dx.doi.org/10.1007/978-0-8176-4619-6_36.

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Swiler, Laura P., Patricia D. Hough, Peter Qian, Xu Xu, Curtis Storlie, and Herbert Lee. "Surrogate Models for Mixed Discrete-Continuous Variables." In Constraint Programming and Decision Making, 181–202. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-04280-0_21.

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Johansson, Christer, and Per Olav Folgerø. "Unit 3 Lesson: Using Reaction Time and Mixed Models." In Neuroaesthetics, 121–35. Cham: Springer International Publishing, 2024. http://dx.doi.org/10.1007/978-3-031-42323-9_9.

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AbstractThis lesson will introduce some concepts related to empirical studies and statistical evaluation. The focus is on evaluating a specified model with controlled fixed factors and several control variables in the context that we have one continuous dependent variable, such as reaction time.
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Günlük, Oktay, Jon Lee, and Janny Leung. "A Polytope for a Product of Real Linear Functions in 0/1 Variables." In Mixed Integer Nonlinear Programming, 513–29. New York, NY: Springer New York, 2011. http://dx.doi.org/10.1007/978-1-4614-1927-3_18.

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Sugasawa, Shonosuke, and Tatsuya Kubokawa. "Small Area Models for Non-normal Response Variables." In Mixed-Effects Models and Small Area Estimation, 83–98. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-19-9486-9_7.

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Conference papers on the topic "Mixed variables":

1

Weld, Christopher, and Lawrence Leemis. "Modeling mixed type random variables." In 2017 Winter Simulation Conference (WSC). IEEE, 2017. http://dx.doi.org/10.1109/wsc.2017.8247900.

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Wang, Min, Maoyin Chen, and Donghua Zhou. "Anomaly Monitoring of Mixture Variables: When Continuous Variables are Mixed Guassian." In 2021 CAA Symposium on Fault Detection, Supervision, and Safety for Technical Processes (SAFEPROCESS). IEEE, 2021. http://dx.doi.org/10.1109/safeprocess52771.2021.9693668.

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Mukherjee, Subhankar, and Pallab Dasgupta. "Incorporating local variables in mixed-signal assertions." In TENCON 2009 - 2009 IEEE Region 10 Conference. IEEE, 2009. http://dx.doi.org/10.1109/tencon.2009.5396176.

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Benavoli, Alessio, and Cassio de Campos. "Bayesian Independence Test with Mixed-type Variables." In 2021 IEEE 8th International Conference on Data Science and Advanced Analytics (DSAA). IEEE, 2021. http://dx.doi.org/10.1109/dsaa53316.2021.9564124.

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Mei, Long Mei, Hashibah Hamid, and Nazrina Aziz. "Variables extraction on large binary variables in discriminant analysis based on mixed variables location model." In INNOVATION AND ANALYTICS CONFERENCE AND EXHIBITION (IACE 2015): Proceedings of the 2nd Innovation and Analytics Conference & Exhibition. AIP Publishing LLC, 2015. http://dx.doi.org/10.1063/1.4937096.

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Iyer, Akshay, Suraj Yerramilli, James M. Rondinelli, Daniel W. Apley, and Wei Chen. "Descriptor Aided Bayesian Optimization for Mixed Variable Materials Design With High Dimensional Qualitative Variables." In ASME 2022 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2022. http://dx.doi.org/10.1115/detc2022-90177.

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Abstract Engineering design often involves qualitative and quantitative design variables, which requires systematic methods for the exploration of these mixed-variable design spaces. Expensive simulation techniques, such as those encountered in materials design, underline the need for efficient search strategies — Bayesian optimization being one of the most widely adopted. Although recent developments in mixed-variable Bayesian optimization have shown promise, the effects of dimensionality of qualitative variables have not been well studied. High dimensional qualitative variables, i.e., with many levels, impose a large design cost as they typically require a larger dataset to quantify the effect of each level on the optimization objective. We address this challenge by leveraging domain knowledge about underlying physical descriptors to infer the effect of unobserved levels that have not been sampled yet. We show that domain knowledge about physical descriptors can be intuitively embedded into the latent variable Gaussian process approach — a mixed-variable GP modeling technique — and used to selectively explore levels of qualitative variables in the Bayesian optimization framework. Our method is robust to certain types of incomplete domain knowledge and significantly reduces the design cost for problems with high-dimensional qualitative variables.
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Daxberger, Erik, Anastasia Makarova, Matteo Turchetta, and Andreas Krause. "Mixed-Variable Bayesian Optimization." In Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}. California: International Joint Conferences on Artificial Intelligence Organization, 2020. http://dx.doi.org/10.24963/ijcai.2020/365.

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The optimization of expensive to evaluate, black-box, mixed-variable functions, i.e. functions that have continuous and discrete inputs, is a difficult and yet pervasive problem in science and engineering. In Bayesian optimization (BO), special cases of this problem that consider fully continuous or fully discrete domains have been widely studied. However, few methods exist for mixed-variable domains and none of them can handle discrete constraints that arise in many real-world applications. In this paper, we introduce MiVaBo, a novel BO algorithm for the efficient optimization of mixed-variable functions combining a linear surrogate model based on expressive feature representations with Thompson sampling. We propose an effective method to optimize its acquisition function, a challenging problem for mixed-variable domains, making MiVaBo the first BO method that can handle complex constraints over the discrete variables. Moreover, we provide the first convergence analysis of a mixed-variable BO algorithm. Finally, we show that MiVaBo is significantly more sample efficient than state-of-the-art mixed-variable BO algorithms on several hyperparameter tuning tasks, including the tuning of deep generative models.
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Comlek, Yigitcan, Liwei Wang, and Wei Chen. "Mixed-Variable Global Sensitivity Analysis With Applications to Data-Driven Combinatorial Materials Design." In ASME 2023 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2023. http://dx.doi.org/10.1115/detc2023-110756.

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Abstract Global Sensitivity Analysis (GSA) is the study of the influence of any given inputs on the outputs of a model. In the context of engineering design, GSA has been widely used to understand both individual and collective contributions of design variables on the design objectives. So far, global sensitivity studies have often been limited to design spaces with only quantitative (numerical) design variables. However, many engineering systems also contain, if not only, qualitative (categorical) design variables in addition to quantitative design variables. In this paper, we integrate the novel Latent Variable Gaussian Process (LVGP) with Sobol’ analysis to develop the first metamodel-based mixed-variable GSA method. Through two analytical case studies, we first validate and demonstrate the effectiveness of our proposed method for mixed-variable problems. Furthermore, while the new metamodel-based mixed-variable GSA method can benefit various engineering design applications, we employ our method with multi-objective Bayesian optimization (BO) to accelerate the Pareto front design exploration in many-level combinatorial design spaces. Specifically, we implement a sensitivity-aware design framework for metal-organic framework (MOF) materials that are constructed only from qualitative design variables and show the benefits of our method for expediting the exploration of novel MOF candidates from a many-level large combinatorial design space.
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Cho, Hyunkyoo, and K. K. Choi. "Iterative Most Probable Point Search Method for Problems With Mixture of Random and Interval Variables." In ASME 2017 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2017. http://dx.doi.org/10.1115/detc2017-67909.

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To represent input variability accurately, input distribution model for random variables should be constructed using many observations or data. However, for certain input variables, engineers may have only their bounds which represent input uncertainty. In practical engineering applications, both random and interval variables could exist at the same time. For the applications, to consider both input variability and uncertainty, inverse reliability analysis should be carried out considering the mixed variables and their mathematical correlation in performance measure. In this paper, an iterative most probable point (MPP) search method has been developed for the mixed variable problem. The random and interval variables update procedures are developed considering the features of mixed variable in the inverse reliability analysis. Both variable update methods proceed one iteration simultaneously to consider the mathematical correlation. An interpolation method is introduced to find better candidate MPP without additional function evaluations. Mixed variable design optimization (MVDO) has been formulated to obtain cost effective and reliable design in the presence of the mixed variables. In addition, the design sensitivity of probabilistic constraint has been developed for effective and efficient MVDO procedure. Using numerical examples, it is found that the developed MPP search method finds accurate MPP more efficiently than generic optimization method. In addition, it is verified that the developed method enables MVDO process with small number of function evaluations.
10

Guo, Cuiping, Junhuan Peng, and Chuantao Li. "Robust estimators in mixed errors-in-variables models." In 2017 IEEE 9th International Conference on Communication Software and Networks (ICCSN). IEEE, 2017. http://dx.doi.org/10.1109/iccsn.2017.8230345.

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Reports on the topic "Mixed variables":

1

Swiler, Laura Painton, Patricia Diane Hough, Peter Qian, Xu Xu, Curtis B. Storlie, and Herbert K. H. Lee. Surrogate models for mixed discrete-continuous variables. Office of Scientific and Technical Information (OSTI), August 2012. http://dx.doi.org/10.2172/1055621.

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Manzano, Osmel, and José Luis Saboin. Investment Booms and Institutions: Implications for the Andean Region. Inter-American Development Bank, May 2022. http://dx.doi.org/10.18235/0004260.

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This paper provides evidence of a positive effect of institutions and reforms in initiating investment booms. We constructed an unbalanced panel of 178 countries for the period 19502019 that considered institutions and reforms at different levels and dimensions. We analyzed the effects of these variables on 159 carefully estimated investment boom episodes, controlling for the standard determinants of investment and using a battery of estimation techniques and robustness checks. Overall, marketoriented and democratic institutions favor the advent of investment booms. Structural reforms present mixed effects and in some cases these are nonlinear. While trade and capital account reform have negative effects, domestic finance, product, and labor market reforms have the opposite. Beyond institutions and reforms, we find different effects regarding external, macro, and structural variables.
3

Gao, Xin, Aiko Kikkawa, and Jong Woo Kang. Evaluating the Impact of Remittances on Human Capital Investment in the Kyrgyz Republic. Asian Development Bank, May 2021. http://dx.doi.org/10.22617/wps210189-2.

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Remittances from overseas can encourage human capital investment, but empirical studies have shown mixed evidence. This paper uses a 5-year panel dataset in the Kyrgyz Republic to examine the impact of remittances on the human capital formation of school-age children. After correcting for endogeneities with instrumental variables, the study finds that remittances have negative impacts on educational achievement. Extended hours of farm labor by children and increased expenditure on durable goods are identified among recipient households. To mitigate negative effects of remittances on children’s learning, the findings call for actions such as financial literacy education and better monitoring of farm labor hours of school-age children.
4

Galeano-Ramírez, Franky Juliano, Nicolás Martínez-Cortés, Carlos D. Rojas-Martínez, and Margaret Guerrero. Nowcasting Colombian Economic Activity: DFM and Factor-MIDAS approaches. Banco de la República, August 2021. http://dx.doi.org/10.32468/be.1168.

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Economic policy decision-making requires constantly assessing the state of economic activity. However, this is not an easy task: official figures have significant lags, and the timely information is usually partial and has different frequencies. This paper applies two types of short-term forecasting methodologies (Factor-MIDAS and DFM) for Colombian economic activity involving information with mixed frequencies. We present a heuristic process to select relevant variables, and we evaluate the proposed models' fits by comparing them with traditional forecasting methodologies. Overall, DFM and Factor-MIDAS forecasts are better than those generated by conventional methodologies, especially as the flow of information increases. In times of COVID-19, the model with the best relative fit was the DFM.
5

Mesquita Moreira, Mauricio, and José Ernesto López Córdova. Regional Integration and Productivity: The Experiences of Brazil and Mexico. Inter-American Development Bank, July 2003. http://dx.doi.org/10.18235/0011120.

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This paper discusses the impacts of integration on productivity, specifically within regional agreements. The paper focuses on the economies of Brazil and Mexico and on their performance in the manufacturing sector. The authors estimate firm-level productivity and test its casual links with trade and foreign direct investment (FDI) variables. The results suggest strong trade related gains, with import discipline emerging as the dominant effect. The results on learning-by-exporting were mixed, with gains restricted to Brazil's regional and worldwide exports. On FDI, foreign firms appear to have had a positive impact on their buyers and suppliers in Mexico, but in Brazil, the overall impact was statistically insignificant on productivity levels and negative on productivity growth.
6

Temple, Brian Allen. Introduction to Mixed Variable Optimization (MVO). Office of Scientific and Technical Information (OSTI), April 2019. http://dx.doi.org/10.2172/1507314.

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Blaisdell, George L., Terry D. Melendy, and Marin N. Blaisdell. Ballistic protection using snow. U.S. Army Engineer Research and Development Center, May 2022. http://dx.doi.org/10.21079/11681/44360.

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Small (5.56 mm, 7.62 mm and 9 mm) and medium (12.7 mm) arms rounds were fired at snow-filled 1.5m cubic gabions in a mid-winter condition in Fairbanks, Alaska. The rounds were excavated and penetration by each ammunition type was measured. A distribution and average of penetration depth was determined. All 320 rounds fired were captured within 1.5m after entering the snow barrier. Comparison with published models of ballistics penetration of snow showed mixed results with several matching our data within 10% and all but one within 32%. However, most of these models are simplistic in that they accommodate limited variables and therefore may not be expected to perform well in all settings. We conclude that snow-based ballistics protection structures can be quickly and efficiently erected in suitable environments and with minimal size, can provide reliable protection against small and medium arms fire.
8

Diakonova, Marina, Corinna Ghirelli, Luis Molina, and Javier J. Pérez. The economic impact of conflict-related and policy uncertainty shocks: the case of Russia. Madrid: Banco de España, November 2022. http://dx.doi.org/10.53479/23707.

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We show how policy uncertainty and conflict-related shocks impact the dynamics of economic activity (GDP) in Russia. We use alternative indicators of “conflict”, relating to specific aspects of this general concept: geopolitical risk, social unrest, outbreaks of political violence and escalations into internal armed conflict. For policy uncertainty we employ the workhorse economic policy uncertainty (EPU) indicator. We use two distinct but complementary empirical approaches. The first is based on a time series mixed-frequency forecasting model. We show that the indicators provide useful information for forecasting GDP in the short run, even when controlling for a comprehensive set of standard high-frequency macro-financial variables. The second approach, is a SVAR model. We show that negative shocks to the selected indicators lead to economic slowdown, with a persistent drop in GDP growth and a short-lived but large increase in country risk.
9

Munk, Jeffrey D., Adewale Odukomaiya, Roderick K. Jackson, and Philip R. Boudreaux. Variable Speed Heat Pump Sizing Guide for Mixed-Humid Climates. Office of Scientific and Technical Information (OSTI), March 2015. http://dx.doi.org/10.2172/1195803.

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Abramson, Mark A., Charles Audet, Jr Dennis, and J. E. Filter Pattern Search Algorithms for Mixed Variable Constrained Optimization Problems. Fort Belvoir, VA: Defense Technical Information Center, June 2004. http://dx.doi.org/10.21236/ada445031.

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