Publications without Fulltext
Permanent URI for this collectionhttps://hdl.handle.net/20.500.14288/3
Browse
9 results
Search Results
Publication Metadata only Overall and pairwise segregation tests based on nearest neighbor contingency tables(Elsevier, 2009) Department of Mathematics; Ceyhan, Elvan; Faculty Member; Department of Mathematics; College of Sciences; N/AMultivariate interaction between two or more classes (or species) has important consequences in many fields and may cause multivariate clustering patterns such as spatial segregation or association. The spatial segregation occurs when members of a class tend to be found near members of the same class (i.e., near conspecifics) while spatial association occurs when members of a class tend to be found near members of the other class or classes. These patterns can be studied using a nearest neighbor contingency table (NNCT). The null hypothesis is randomness in the nearest neighbor (NN) structure, which may result from - among other patterns - random labeling (RL) or complete spatial randomness (CSR) of points from two or more classes (which is called the CSR independence, henceforth). New versions of overall and cell-specific tests based on NNCTs (i.e., NNCT-tests) are introduced and compared with Dixon's overall and cell-specific tests and various other spatial clustering methods. Overall segregation tests are used to detect any deviation from the null case, while the cell-specific tests are post hoc pairwise spatial interaction tests that are applied when the overall test yields a significant result. The distributional properties of these tests are analyzed and finite sample performance of the tests are assessed by an extensive Monte Carlo simulation study. Furthermore, it is shown that the new NNCT-tests have better performance in terms of Type I error and power estimates. The methods are also applied on two real life data sets for illustrative purposes. (c) 2008 Elsevier B.V. All rights reserved.Publication Metadata only Class-specific tests of spatial segregation based on nearest neighbor contingency tables(Wiley, 2009) Department of Mathematics; Ceyhan, Elvan; Faculty Member; Department of Mathematics; College of Sciences; N/AThe spatial interaction between two or more classes might cause multivariate clustering patterns such as segregation or association, which can be tested using a nearest neighbor contingency table (NNCT). The null hypothesis is randomness in the nearest neighbor structure, which may result from random labeling (RL) or complete spatial randomness of points from two or more classes (which is henceforth called CSR independence). We consider Dixon's class-specific segregation test and introduce a new class-specific test, which is a new decomposition of Dixon's overall chi-squared segregation statistic. We analyze the distributional properties and compare the empirical significant levels and power estimates of the tests using extensive Monte Carlo simulations. We demonstrate that the new class-specific tests have comparable performance with the currently available tests based on NNCTs. For illustrative purposes, we use three example data sets and provide guidelines for using these tests.Publication Metadata only A Bayesian generalized linear model for Crimean–Congo hemorrhagic fever incidents(Springer, 2018) Ryu, Duchwan; Bilgili, Devrim; Liang, Faming; Ebrahimi, Nader; Ergönül, Önder; Faculty Member; School of Medicine; 110398Global spread of the Crimean-Congo hemorrhagic fever (CCHF) is a fatal viral infection disease found in parts of Africa, Asia, Eastern Europe and Middle East, with a fatality rate of up to 30%. A timely prediction of the prevalence of CCHF incidents is highly desirable, while CCHF incidents often exhibit nonlinearity in both temporal and spatial features. However, the modeling of discrete incidents is not trivial. Moreover, the CCHF incidents are monthly observed in a long period and take a nonlinear pattern over a region at each time point. Hence, the estimation and the data assimilation for incidents require extensive computations. In this paper, using the data augmentation with latent variables, we propose to utilize a dynamically weighted particle filter to take advantage of its population controlling feature in data assimilation. We apply our approach in an analysis of monthly CCHF incidents data collected in Turkey between 2004 and 2012. The results indicate that CCHF incidents are higher at Northern Central Turkey during summer and that some beforehand interventions to stop the propagation are recommendable. Supplementary materials accompanying this paper appear on-line.Publication Metadata only Nonconvex homogenization for one-dimensional controlled random walks in random potential(2019) Zeitouni, Ofer; Department of Mathematics; Yılmaz, Atilla; Faculty Member; Department of Mathematics; College of Sciences; N/AWe consider a finite horizon stochastic optimal control problem for nearest-neighbor random walk {Xi} on the set of integers. The cost function is the expectation of the exponential of the path sum of a random stationary and ergodic bounded potential plus θXn. The random walk policies are measurable with respect to the random potential, and are adapted, with their drifts uniformly bounded in magnitude by a parameter δ∈[0,1]. Under natural conditions on the potential, we prove that the normalized logarithm of the optimal cost function converges. The proof is constructive in the sense that we identify asymptotically optimal policies given the value of the parameter δ, as well as the law of the potential. It relies on correctors from large deviation theory as opposed to arguments based on subadditivity which do not seem to work except when δ=0. The Bellman equation associated to this control problem is a second-order Hamilton–Jacobi (HJ) partial difference equation with a separable random Hamiltonian which is nonconvex in θ unless δ=0. We prove that this equation homogenizes under linear initial data to a first-order HJ equation with a deterministic effective Hamiltonian. When δ=0, the effective Hamiltonian is the tilted free energy of random walk in random potential and it is convex in θ. In contrast, when δ=1, the effective Hamiltonian is piecewise linear and nonconvex in θ. Finally, when δ∈ (0,1), the effective Hamiltonian is expressed completely in terms of the tilted free energy for the δ=0 case and its convexity/nonconvexity in θ is characterized by a simple inequality involving δ and the magnitude of the potential, thereby marking two qualitatively distinct control regimes.Publication Metadata only Posterior-predictive evidence on us inflation using extended new keynesian phillips curve models with non-filtered data(Wiley, 2014) Basturk, Nalan; Ceyhan, S. Pinar; Van Dijk, Herman K.; Department of Economics; Çakmaklı, Cem; Faculty Member; Department of Economics; College of Administrative Sciences and Economics; 107818Changing time series properties of US inflation and economic activity, measured as marginal costs, are modeled within a set of extended New Keynesian Phillips curve (NKPC) models. It is shown that mechanical removal or modeling of simple low-frequency movements in the data may yield poor predictive results which depend on the model specification used. Basic NKPC models are extended to include structural time series models that describe typical time-varying patterns in levels and volatilities. Forward- and backward-looking expectation components for inflation are incorporated and their relative importance is evaluated. Survey data on expected inflation are introduced to strengthen the information in the likelihood. Use is made of simulation-based Bayesian techniques for the empirical analysis. No credible evidence is found on endogeneity and long-run stability between inflation and marginal costs. Backward-looking inflation appears stronger than forward-looking inflation. Levels and volatilities of inflation are estimated more precisely using rich NKPC models. The extended NKPC structures compare favorably with existing basic Bayesian vector autoregressive and stochastic volatility models in terms of fit and prediction. Tails of the complete predictive distributions indicate an increase in the probability of deflation in recent years.Publication Metadata only Testing for the stochastic dominance efficiency of a given portfolio(Wiley-Blackwell, 2014) Linton, Oliver; Whang, Yoon-Jae; N/A; Post, Gerrit Tjeerd; Other; Graduate School of Business; N/AWe propose a new statistical test of the stochastic dominance efficiency of a given portfolio over a class of portfolios. We establish its null and alternative asymptotic properties, and define a method for consistently estimating critical values. We present some numerical evidence that our tests work well in moderate-sized samples.Publication Metadata only The design of optimum component test plans for system reliability(Elsevier, 2006) Feyzioğlu, Orhan; Altınel, İ. Kuban; Department of Industrial Engineering; Özekici, Süleyman; Faculty Member; Department of Industrial Engineering; College of Engineering; 32631It is often necessary to conduct individual component tests for the prediction and verification of system reliability. Moreover, system testing can be economically infeasible or even impossible. The question then arises as to how component test plans should be designed so as to minimize test costs. Acceptance procedures based on the sum of failures during component tests have been previously suggested in the literature only for a series system, parallel system, and a serial connection of redundant subsystems. This line of research is extended by considering serial connection of standby redundant and k-out-of-n subsystems. It is shown that the serial connection of the mixture of all these systems can also be modelled. The optimal component testing problem is formulated as a semi-infinite linear program, and a procedure to compute optimum component test times is developed. The solution procedure is based on the well known cutting plane idea and column generation technique. Numerical examples are also provided.Publication Metadata only Error analysis of statistical linearization with gaussian closure for large-degree-of-freedom systems(Elsevier Sci Ltd, 1998) Micaletti, RC; Cakmak, AS; Nielsen, SRK; Department of Mathematics; Köylüoğlu, Hasan Uğur; Teaching Faculty; Department of Mathematics; College of Sciences; N/AThis paper contains an analysis of the error induced by applying the method of equivalent statistical linearization (ESL) to randomly excited multi-degree-of-freedom (m.d.f.) geometrically nonlinear shear-frame structures as the number of degrees of freedom increases. The quantity that is analyzed is the variance of the top-story displacement. The m.d.f. systems under consideration obtain their nonlinearity through cubic polynomial interstory restoring forces and the external excitation is modeled as the stationary output of a Kanai-Tajimi filter. Parameters of the filter and the m.d.f. structures, as well as the intensity of the gaussian white noise, are calibrated such that quantitative comparisons of the error between the exact solutions, estimated from Monte Carlo simulations, and the ESL solutions are possible among systems of different dimensions.Publication Metadata only Comparison of relative density of two random geometric digraph families in testing spatial clustering(Springer, 2014) Department of Mathematics; Ceyhan, Elvan; Faculty Member; Department of Mathematics; College of Sciences; N/AWe compare the performance of relative densities of two parameterized random geometric digraph families called proximity catch digraphs (PCDs) in testing bivariate spatial patterns. These PCD families are proportional edge (PE) and central similarity (CS) PCDs and are defined with proximity regions based on relative positions of data points from two classes. The relative densities of these PCDs were previously used as statistics for testing segregation and association patterns against complete spatial randomness. The relative density of a digraph, D, with n vertices (i.e., with order n) represents the ratio of the number of arcs in D to the number of arcs in the complete symmetric digraph of the same order. When scaled properly, the relative density of a PCD is a U-statistic; hence, it has asymptotic normality by the standard central limit theory of U-statistics. The PE- and CS-PCDs are defined with an expansion parameter that determines the size or measure of the associated proximity regions. In this article, we extend the distribution of the relative density of CS-PCDs for expansion parameter being larger than one, and compare finite sample performance of the tests by Monte Carlo simulations and asymptotic performance by Pitman asymptotic efficiency. We find the optimal expansion parameters of the PCDs for testing each alternative in finite samples and in the limit as the sample size tending to infinity. As a result of our comparisons, we demonstrate that in terms of empirical power (i.e., for finite samples) relative density of CS-PCD has better performance (which occurs for expansion parameter values larger than one) for the segregation alternative, while relative density of PE-PCD has better performance for the association alternative. The methods are also illustrated in a real-life data set from plant ecology.