Approximate Maximum Likelihood Method Research Articles

ПОРОГОВІ МОДЕЛІ ДЛЯ ПРОЦЕСІВ ЛЕВІ ТА ОЦІНКА НАБЛИЖЕНОЇ МАКСИМАЛЬНОЇ ПРАВДОПОДІБНОСТІ

Using the Levy process (the solution to the Ito–Skorokhod stochastic differential equation) we propose the construction of the model of the threshold process and the approximate maximum likelihood method based on approximation of the logarithmic function of the likelihood of observations. The estimates for the parameters of the two-mode threshold jump process with discretely sampled data are found. We show that by checking the likelihood ratio, determining the presence of threshold effects is possible. Keywords: threshold jump process, approximate maximum likelihood method, stochastic differential equation.

An Approximate Maximum Likelihood Method for the Joint Estimation of Range and Doppler of Multiple Targets in OFDM-Based Radar Systems

An Approximate Maximum Likelihood Method for the Joint Estimation of Range and Doppler of Multiple Targets in OFDM-Based Radar Systems

A Statistical Dependence Framework Based on a Multivariate Normal Copula Function and Stochastic Differential Equations for Multivariate Data in Forestry

Stochastic differential equations and Copula theories are important topics that have many advantages for applications in almost every discipline. Many studies in forestry collect longitudinal, multi-dimensional, and discrete data for which the amount of measurement of individual variables does not match. For example, during sampling experiments, the diameters of all trees, the heights of approximately 10% of the trees, and the tree crown base height and crown width for a significantly smaller number of trees are measured. In this study, for estimating five-dimensional dependencies, we used a normal copula approach, where the dynamics of individual tree variables (diameter, potentially available area, height, crown base height, and crown width) are described by a stochastic differential equation with mixed-effect parameters. The approximate maximum likelihood method was used to obtain parameter estimates of the presented stochastic differential equations, and the normal copula dependence parameters were estimated using the pseudo-maximum likelihood method. This study introduced the normalized multi-dimensional interaction information index based on differential entropy to capture dependencies between state variables. Using conditional copula-type probability density functions, the exact form equations defining the links among the diameter, potentially available area, height, crown base height, and crown width were derived. All results were implemented in the symbolic algebra system MAPLE.

Spatiotemporal Distribution and Genetic Characterization of Measles Strains Circulating in Greece during the 2017-2018 Outbreak.

Between May 2017 and November 2018, Greece has experienced a severe measles outbreak with a total of 3258 cases reported, after reaching its goal of eliminating measles since 2014–2015. In this study, we aimed to investigate the origin and the dispersal patterns of the measles strains that circulated in Greece during this outbreak and to identify possible transmission patterns of measles virus (MeV) in the country. Of the 832 measles suspect cases referred to the National Measles and Rubella Reference Laboratory for MeV RNA detection, 131 randomly selected positive samples, representative of the temporal and spatial distribution of the laboratory-confirmed measles cases in Greece, were processed for genotypic identification by an RT-PCR amplification of a 598 bp fragment containing the 450 bp hypervariable region of the measles virus N gene. Phylogenetic analysis was carried out by the approximate maximum likelihood method (ML) under the generalized time-reversible (GTR + cat) model. All samples analyzed were found to belong to genotype B3. Comparative analysis with other European and reference measles strains revealed three separate major clusters and other multiple viruses circulating simultaneously in Greece. They were all isolated from three main community groups, Greek-Roma children, non-minority Greek nationals and immigrants/refugees, a finding that is in accordance with what was also observed in the last two measles outbreaks in 2005–2006 and 2010–2011. Notably, for one of the three clusters, no similarity was detected with previously reported prototype strains. Our results indicate the need for a more intensive vaccination program against measles amongst minority populations and in refugee hot-spots as well as the importance of molecular surveillance as a tool for monitoring measles outbreaks.

A marginal moment matching approach for fitting endemic-epidemic models to underreported disease surveillance counts.

Count data are often subject to underreporting, especially in infectious disease surveillance. We propose an approximate maximum likelihood method to fit count time series models from the endemic-epidemic class to underreported data. The approach is based on marginal moment matching where underreported processes are approximated through completely observed processes from the same class. Moreover, the form of the bias when underreporting is ignored or taken into account via multiplication factors is analyzed. Notably, we show that this leads to a downward bias in model-based estimates of the effective reproductive number. A marginal moment matching approach can also be used to account for reporting intervals which are longer than the mean serial interval of a disease. The good performance of the proposed methodology is demonstrated in simulation studies. An extension to time-varying parameters and reporting probabilities is discussed and applied in a case study on weekly rotavirus gastroenteritis counts in Berlin,Germany.

Selecting between causal and noncausal models with quantile autoregressions

Abstract We propose a model selection criterion to detect purely causal from purely noncausal models in the framework of quantile autoregressions (QAR). We also present asymptotics for the i.i.d. case with regularly varying distributed innovations in QAR. This new modelling perspective is appealing for investigating the presence of bubbles in economic and financial time series, and is an alternative to approximate maximum likelihood methods. We illustrate our analysis using hyperinflation episodes of Latin American countries.

Approximate maximum likelihood estimation of a threshold diffusion process

In order to estimate the parameters of a two-regime threshold diffusion process with discretely sampled data, an approximate maximum likelihood method (AMLE) based on approximating the log-likelihood function of the observations is proposed. Both the drift and the diffusion terms are allowed to be either linear or non-linear. In order to choose the most appropriate among these four possibilities, three information criteria are employed. Further, a likelihood ratio test can help to determine whether threshold effects are present. Via simulations, the finite sample performance of the proposed AMLE is compared to an alternative quasi-likelihood estimator and the finite sample performance of the information criteria as well as the likelihood ratio test are studied. Finally, the efficacy of our approach is demonstrated with two financial time series.

Automatic generation of evolutionary hypotheses using mixed Gaussian phylogenetic models

Phylogenetic comparative methods are widely used to understand and quantify the evolution of phenotypic traits, based on phylogenetic trees and trait measurements of extant species. Such analyses depend crucially on the underlying model. Gaussian phylogenetic models like Brownian motion and Ornstein-Uhlenbeck processes are the workhorses of modeling continuous-trait evolution. However, these models fit poorly to big trees, because they neglect the heterogeneity of the evolutionary process in different lineages of the tree. Previous works have addressed this issue by introducing shifts in the evolutionary model occurring at inferred points in the tree. However, for computational reasons, in all current implementations, these shifts are "intramodel," meaning that they allow jumps in 1 or 2 model parameters, keeping all other parameters "global" for the entire tree. There is no biological reason to restrict a shift to a single model parameter or, even, to a single type of model. Mixed Gaussian phylogenetic models (MGPMs) incorporate the idea of jointly inferring different types of Gaussian models associated with different parts of the tree. Here, we propose an approximate maximum-likelihood method for fitting MGPMs to comparative data comprising possibly incomplete measurements for several traits from extant and extinct phylogenetically linked species. We applied the method to the largest published tree of mammal species with body- and brain-mass measurements, showing strong statistical support for an MGPM with 12 distinct evolutionary regimes. Based on this result, we state a hypothesis for the evolution of the brain-body-mass allometry over the past 160 million y.

Reliability estimation in a multicomponent stress–strength model under generalized half-normal distribution based on progressive type-II censoring

ABSTRACTIn this paper, based on progressively Type-II censored samples, the problem of estimation of multicomponent stress–strength reliability under generalized half-normal (GHN) distribution is considered. The reliability of a k-component stress-strength system is estimated when both stress and strength variates are assumed to have a GHN distribution with various cases of same and different shape and scale parameters. Different methods such as the maximum likelihood estimates (MLEs) and Bayes estimation are discussed. The expectation maximization algorithm and approximate maximum likelihood methods are proposed to compute the MLE of reliability. The Lindley's approximation method, as well as Metropolis–Hastings algorithm, are applied to compute Bayes estimates. The performance of the proposed procedures is also demonstrated via a Monte Carlo simulation study and an illustrative example.

Robust Cochlear Modiolar Axis Detection in CT.

The cochlea, the auditory part of the inner ear, is a spiral-shaped organ with large morphological variability. An individualized assessment of its shape is essential for clinical applications related to tonotopy and cochlear implantation. To unambiguously reference morphological parameters, reliable recognition of the cochlear modiolar axis in computed tomography (CT) images is required. The conventional method introduces measurement uncertainties, as it is based on manually selected and difficult to identify landmarks. Herein, we present an algorithm for robust modiolar axis detection in clinical CT images. We define the modiolar axis as the rotation component of the kinematic spiral motion inherent in the cochlear shape. For surface fitting, we use a compact shape representation in a 7-dimensional kinematic parameter space based on extended Plücker coordinates. It is the first time such a kinematic representation is used for shape analysis in medical images. Robust surface fitting is achieved with an adapted approximate maximum likelihood method assuming a Student-t distribution, enabling axis detection even in partially available surface data. We verify the algorithm performance on a synthetic data set with cochlear surface subsets. In addition, we perform an experimental study with four experts in 23 human cochlea CT data sets to compare the automated detection with the manually found axes. Axes found from co-registered high resolution μCT scans are used for reference. Our experiments show that the algorithm reduces the alignment error providing more reliable modiolar axis detection for clinical and research applications.

Approximate composite marginal likelihood inference in spatial generalized linear mixed models

ABSTRACTNon-Gaussian spatial responses are usually modeled using spatial generalized linear mixed model with spatial random effects. The likelihood function of this model cannot usually be given in a closed form, thus the maximum likelihood approach is very challenging. There are numerical ways to maximize the likelihood function, such as Monte Carlo Expectation Maximization and Quadrature Pairwise Expectation Maximization algorithms. They can be applied but may in such cases be computationally very slow or even prohibitive. Gauss–Hermite quadrature approximation only suitable for low-dimensional latent variables and its accuracy depends on the number of quadrature points. Here, we propose a new approximate pairwise maximum likelihood method to the inference of the spatial generalized linear mixed model. This approximate method is fast and deterministic, using no sampling-based strategies. The performance of the proposed method is illustrated through two simulation examples and practical aspects are investigated through a case study on a rainfall data set.

Estimating a class of diffusions from discrete observations via approximate maximum likelihood method

ABSTRACTAn approximate maximum likelihood method of estimation of diffusion parameters based on discrete observations of a diffusion X along fixed time-interval and Euler approximation of integrals is analysed. We assume that X satisfies a stochastic differential equation (SDE) of form , with non-random initial condition. SDE is nonlinear in generally. Based on assumption that maximum likelihood estimator of the drift parameter based on continuous observation of a path over exists we prove that measurable estimator of the parameters obtained from discrete observations of X along by maximization of the approximate log-likelihood function exists, being consistent and asymptotically normal, and tends to zero with rate in probability when tends to zero with T fixed. The same holds in case of an ergodic diffusion when T goes to infinity in a way that goes to zero with equidistant sampling, and we applied these to show consistency and asymptotical normality of , and asymptotic efficiency of in this case.

Tropical rainforests that persisted: inferences from the Quaternary demographic history of eight tree species in the Guiana shield

How Quaternary climatic and geological disturbances influenced the composition of Neotropical forests is hotly debated. Rainfall and temperature changes during and/or immediately after the last glacial maximum (LGM) are thought to have strongly affected the geographical distribution and local abundance of tree species. The paucity of the fossil records in Neotropical forests prevents a direct reconstruction of such processes. To describe community-level historical trends in forest composition, we turned therefore to inferential methods based on the reconstruction of past demographic changes. In particular, we modelled the history of rainforests in the eastern Guiana Shield over a timescale of several thousand generations, through the application of approximate Bayesian computation and maximum-likelihood methods to diversity data at nuclear and chloroplast loci in eight species or subspecies of rainforest trees. Depending on the species and on the method applied, we detected population contraction, expansion or stability, with a general trend in favour of stability or expansion, with changes presumably having occurred during or after the LGM. These findings suggest that Guiana Shield rainforests have globally persisted, while expanding, through the Quaternary, but that different species have experienced different demographic events, with a trend towards the increase in frequency of light-demanding, disturbance-associated species.

On Calibration of Three-Axis Magnetometer

Magnetometer has received wide applications in attitude determination and scientific measurements. Calibration is an important step for any practical magnetometer use. The most popular three-axis magnetometer calibration methods are attitude-independent and have been founded on an approximate maximum likelihood (ML) estimation with a quartic subjective function, derived from the fact that the magnitude of the calibrated measurements should be constant in a homogeneous magnetic field. This paper highlights the shortcomings of those popular methods and proposes to use the quadratic optimal ML estimation instead for magnetometer calibration. Simulation and test results show that the optimal ML calibration is superior to the approximate ML methods for magnetometer calibration in both accuracy and stability, especially for those situations without sufficient attitude excitation. The significant benefits deserve the moderately increased computation burden. The main conclusion obtained in the context of magnetometer in this paper is potentially applicable to various kinds of three-axis sensors.

Influenza virus drug resistance: a time-sampled population genetics perspective.

The challenge of distinguishing genetic drift from selection remains a central focus of population genetics. Time-sampled data may provide a powerful tool for distinguishing these processes, and we here propose approximate Bayesian, maximum likelihood, and analytical methods for the inference of demography and selection from time course data. Utilizing these novel statistical and computational tools, we evaluate whole-genome datasets of an influenza A H1N1 strain in the presence and absence of oseltamivir (an inhibitor of neuraminidase) collected at thirteen time points. Results reveal a striking consistency amongst the three estimation procedures developed, showing strongly increased selection pressure in the presence of drug treatment. Importantly, these approaches re-identify the known oseltamivir resistance site, successfully validating the approaches used. Enticingly, a number of previously unknown variants have also been identified as being positively selected. Results are interpreted in the light of Fisher's Geometric Model, allowing for a quantification of the increased distance to optimum exerted by the presence of drug, and theoretical predictions regarding the distribution of beneficial fitness effects of contending mutations are empirically tested. Further, given the fit to expectations of the Geometric Model, results suggest the ability to predict certain aspects of viral evolution in response to changing host environments and novel selective pressures.

Fast Maximum Likelihood Localization of Near-Field Sources

Conventional methods for locating near-field sources generally suffer performance degradation when the assumption of uniform spatial Gaussian noise does not hold. In this paper study the scenario of non-uniform spatial Gaussian noise. First we construct the near-field signal model based on planar sensor array and derive the maximum likelihood method for obtaining the azimuth and distance of sound sources, then we proposed two fast algorithms-stepwise-concentrated maximum likelihood method(SML) and approximate maximum likelihood method(AML) to reduce the high computational complexity of maximum likelihood localization method. Simulation results show that the two proposed methods outperform conventional maximum likelihood method, with lower computational complexity and less mean squared error of both azimuth estimation and distance estimation.

A note on tail dependence regression

In financial practice, it is important to understand the dependence structure between the returns of individual assets and the market index. This is particularly true under extreme situations. Theoretically, this amounts to regressing the dependence relationship against a set of pre-specified predictive variables. To this end, we propose here a novel method called tail dependence regression. It assumes a tail dependence index model between individual assets and market index. Subsequently, such a tail dependence index is modeled as a linear combination of the predictors through a monotonic transformation. An approximate maximum likelihood method is then developed to estimate the unknown regression coefficients. The resulting estimator’s asymptotic properties are investigated theoretically. Numerical studies including both simulated and real datasets are presented for illustration purposes.

A Note on Tail Dependence Regression

In financial practice, it is important to understand the dependence structure between the returns of individual assets and the market index. This particularly true under extreme situations. Theoretically, this amounts to regress the dependence relationship against a set of pre-specified predictive variables. To this end, we propose here a novel method called tail dependence regression. It assumes a tail dependence index model between individual assets and market index. Subsequently, such a tail dependence index is modeled as a linear combination of the predictors through a monotonic transformation. An approximate maximum likelihood method is then developed to estimate the unknown regression coefficients. The resulting estimator’s asymptotic properties are investigated theoretically. Numerical studies including both simulated and real datasets are presented for illustration purpose.

Generalized, basis-independent kinematic surface fitting

Kinematic surfaces form a general class of surfaces, including surfaces of revolution, helices, spirals, and more. Standard methods for fitting such surfaces are either specialized to a small subset of these surface types (either focusing exclusively on cylinders or exclusively on surfaces of revolution) or otherwise are basis-dependent (leading to scale-dependent results). Previous work has suggested re-scaling data to a fixed size bounding box to avoid the basis-dependence issues. We show that this method fails on some simple, common cases such as a box or a cone with small noise. We propose instead adapting a well-studied approximate maximum-likelihood method to the kinematic surface fitting problem, which solves the basis-dependence issue. Because this technique is not designed for a specific type of kinematic surface, it also opens the door to the possibility of new variants of kinematic surfaces, such as affinely-scaled surfaces of revolution.

Estimation of the Exponential Distributions based on Multiply Progressive Type II Censored Sample

The maximum likelihood(ML) estimation of the scale parameters of an exponential distribution based on progressive Type II censored samples is given. The sample is multiply censored (some middle observations being censored); however, the ML method does not admit explicit solutions. In this paper, we propose multiply progressive Type II censoring. This paper presents the statistical inference on the scale parameter for the exponential distribution when samples are multiply progressive Type II censoring. The scale parameter is estimated by approximate ML methods that use two different Taylor series expansion types (<TEX>$AMLE_I$</TEX>, <TEX>$AMLE_{II}$</TEX>). We also obtain the maximum likelihood estimator(MLE) of the scale parameter under the proposed multiply progressive Type II censored samples. We compare the estimators in the sense of the mean square error(MSE). The simulation procedure is repeated 10,000 times for the sample size n = 20 and 40 and various censored schemes. The <TEX>$AMLE_{II}$</TEX> is better than MLE and <TEX>$AMLE_I$</TEX> in the sense of the MSE.