94 resultados para Maximum Likelihood Estimation


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The traditional searching method for model-order selection in linear regression is a nested full-parameters-set searching procedure over the desired orders, which we call full-model order selection. On the other hand, a method for model-selection searches for the best sub-model within each order. In this paper, we propose using the model-selection searching method for model-order selection, which we call partial-model order selection. We show by simulations that the proposed searching method gives better accuracies than the traditional one, especially for low signal-to-noise ratios over a wide range of model-order selection criteria (both information theoretic based and bootstrap-based). Also, we show that for some models the performance of the bootstrap-based criterion improves significantly by using the proposed partial-model selection searching method. Index Terms— Model order estimation, model selection, information theoretic criteria, bootstrap 1. INTRODUCTION Several model-order selection criteria can be applied to find the optimal order. Some of the more commonly used information theoretic-based procedures include Akaike’s information criterion (AIC) [1], corrected Akaike (AICc) [2], minimum description length (MDL) [3], normalized maximum likelihood (NML) [4], Hannan-Quinn criterion (HQC) [5], conditional model-order estimation (CME) [6], and the efficient detection criterion (EDC) [7]. From a practical point of view, it is difficult to decide which model order selection criterion to use. Many of them perform reasonably well when the signal-to-noise ratio (SNR) is high. The discrepancies in their performance, however, become more evident when the SNR is low. In those situations, the performance of the given technique is not only determined by the model structure (say a polynomial trend versus a Fourier series) but, more importantly, by the relative values of the parameters within the model. This makes the comparison between the model-order selection algorithms difficult as within the same model with a given order one could find an example for which one of the methods performs favourably well or fails [6, 8]. Our aim is to improve the performance of the model order selection criteria in cases where the SNR is low by considering a model-selection searching procedure that takes into account not only the full-model order search but also a partial model order search within the given model order. Understandably, the improvement in the performance of the model order estimation is at the expense of additional computational complexity.

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Maximum-likelihood estimates of the parameters of stochastic differential equations are consistent and asymptotically efficient, but unfortunately difficult to obtain if a closed-form expression for the transitional probability density function of the process is not available. As a result, a large number of competing estimation procedures have been proposed. This article provides a critical evaluation of the various estimation techniques. Special attention is given to the ease of implementation and comparative performance of the procedures when estimating the parameters of the Cox–Ingersoll–Ross and Ornstein–Uhlenbeck equations respectively.

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Sequence data often have competing signals that are detected by network programs or Lento plots. Such data can be formed by generating sequences on more than one tree, and combining the results, a mixture model. We report that with such mixture models, the estimates of edge (branch) lengths from maximum likelihood (ML) methods that assume a single tree are biased. Based on the observed number of competing signals in real data, such a bias of ML is expected to occur frequently. Because network methods can recover competing signals more accurately, there is a need for ML methods allowing a network. A fundamental problem is that mixture models can have more parameters than can be recovered from the data, so that some mixtures are not, in principle, identifiable. We recommend that network programs be incorporated into best practice analysis, along with ML and Bayesian trees.

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Phylogenetic relationships within the Tabanidae are largely unknown, despite their considerable medical and ecological importance. The first robust phylogenetic hypothesis for the horse fly tribe Scionini is provided, completing the systematic placement of all tribes in the subfamily Pangoniinae. The Scionini consists of seven mostly southern hemisphere genera distributed in Australia, New Guinea, New Zealand and South America. A 5757. bp alignment of 6 genes, including mitochondrial (COI and COII), ribosomal (28S) and nuclear (AATS and CAD regions 1, 3 and 4) genes, was analysed for 176 taxa using both Bayesian and maximum likelihood approaches. Results indicate the Scionini are strongly monophyletic, with the exclusion of the only northern hemisphere genus Goniops. The South American genera Fidena, Pityocera and Scione were strongly monophyletic, corresponding to current morphology-based classification schemes. The most widespread genus Scaptia was paraphyletic and formed nine strongly supported monophyletic clades, each corresponding to either the current subgenera or several previously synonymised genera that should be formally resurrected. Molecular results also reveal a newly recognised genus endemic to New Zealand, formerly placed within Scaptia. Divergence time estimation was employed to assess the global biogeographical patterns in the Pangoniinae. These analyses demonstrated that the Scionini are a typical Gondwanan group whose diversification was influenced by the fragmentation of that ancient land mass. Furthermore, results indicate that the Scionini most likely originated in Australia and subsequently radiated to New Zealand and South American by both long distance dispersal and vicariance. The phylogenetic framework of the Scionini provided herein will be valuable for taxonomic revisions of the Tabanidae.

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A "self-exciting" market is one in which the probability of observing a crash increases in response to the occurrence of a crash. It essentially describes cases where the initial crash serves to weaken the system to some extent, making subsequent crashes more likely. This thesis investigates if equity markets possess this property. A self-exciting extension of the well-known jump-based Bates (1996) model is used as the workhorse model for this thesis, and a particle-filtering algorithm is used to facilitate estimation by means of maximum likelihood. The estimation method is developed so that option prices are easily included in the dataset, leading to higher quality estimates. Equilibrium arguments are used to price the risks associated with the time-varying crash probability, and in turn to motivate a risk-neutral system for use in option pricing. The option pricing function for the model is obtained via the application of widely-used Fourier techniques. An application to S&P500 index returns and a panel of S&P500 index option prices reveals evidence of self excitation.

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The estimation of the critical gap has been an issue since the 1970s, when gap acceptance was introduced to evaluate the capacity of unsignalized intersections. The critical gap is the shortest gap that a driver is assumed to accept. A driver’s critical gap cannot be measured directly and a number of techniques have been developed to estimate the mean critical gaps of a sample of drivers. This paper reviews the ability of the Maximum Likelihood technique and the Probability Equilibrium Method to predict the mean and standard deviation of the critical gap with a simulation of 100 drivers, repeated 100 times for each flow condition. The Maximum Likelihood method gave consistent and unbiased estimates of the mean critical gap. Whereas the probability equilibrium method had a significant bias that was dependent on the flow in the priority stream. Both methods were reasonably consistent, although the Maximum Likelihood Method was slightly better. If drivers are inconsistent, then again the Maximum Likelihood method is superior. A criticism levelled at the Maximum Likelihood method is that a distribution of the critical gap has to be assumed. It was shown that this does not significantly affect its ability to predict the mean and standard deviation of the critical gaps. Finally, the Maximum Likelihood method can predict reasonable estimates with observations for 25 to 30 drivers. A spreadsheet procedure for using the Maximum Likelihood method is provided in this paper. The PEM can be improved if the maximum rejected gap is used.

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Osteoporosis is a disease characterized by low bone mineral density (BMD) and poor bone quality. Peak bone density is achieved by the third decade of life, after which bone is maintained by a balanced cycle of bone resorption and synthesis. Age-related bone loss occurs as the bone resorption phase outweighs the bone synthesis phase of bone metabolism. Heritability accounts for up to 90% of the variability in BMD. Chromosomal loci including 1p36, 2p22-25, 11q12-13, parathyroid hormone receptor type 1 (PTHR1), interleukin-6 (IL-6), interleukin 1 alpha (IL-1α) and type II collagen A1/vitamin D receptor (COL11A1/VDR) have been linked or shown suggestive linkage with BMD in other populations. To determine whether these loci predispose to low BMD in the Irish population, we investigated 24 microsatellite markers at 7 chromosomal loci by linkage studies in 175 Irish families of probands with primary low BMD (T-score ≤ -1.5). Nonparametric analysis was performed using the maximum likelihood variance estimation and traditional Haseman-Elston tests on the Mapmaker/Sibs program. Suggestive evidence of linkage was observed with lumbar spine BMD at 2p22-25 (maximum LOD score 2.76) and 11q12-13 (MLS 2.55). One region, 1p36, approached suggestive linkage with femoral neck BMD (MLS 2.17). In addition, seven markers achieved LOD scores > 1.0, D2S149, D11S1313, D11S987, D11S1314 including those encompassing the PTHR1 (D3S3559, D3S1289) for lumbar spine BMD and D2S149 for femoral neck BMD. Our data suggest that genes within a these chromosomal regions are contributing to a predisposition to low BMD in the Irish population.

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Background: A knowledge of energy expenditure in infancy is required for the estimation of recommended daily amounts of food energy, for designing artificial infant feeds, and as a reference standard for studies of energy metabolism in disease states. Objectives: The objectives of this study were to construct centile reference charts for total energy expenditure (TEE) in infants across the first year of life. Methods: Repeated measures of TEE using the doubly labeled water technique were made in 162 infants at 1.5, 3, 6, 9 and 12 months. In total, 322 TEE measurements were obtained. The LMS method with maximum penalized likelihood was used to construct the centile reference charts. Centiles were constructed for TEE expressed as MJ/day and also expressed relative to body weight (BW) and fat-free mass (FFM). Results: TEE increased with age and was 1.40,1.86, 2.64, 3.07 and 3.65 MJ/day at 1.5, 3, 6, 9 and 12 months, respectively. The standard deviations were 0.43, 0.47, 0.52,0.66 and 0.88, respectively. TEE in MJ/kg increased from 0.29 to 0.36 and in MJ/day/kg FFM from 0.36 to 0.48. Conclusions: We have presented centile reference charts for TEE expressed as MJ/day and expressed relative to BW and FFM in infants across the first year of life. There was a wide variation or biological scatter in TEE values seen at all ages. We suggest that these centile charts may be used to assess and possibly quantify abnormal energy metabolism in disease states in infants.

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This article describes a maximum likelihood method for estimating the parameters of the standard square-root stochastic volatility model and a variant of the model that includes jumps in equity prices. The model is fitted to data on the S&P 500 Index and the prices of vanilla options written on the index, for the period 1990 to 2011. The method is able to estimate both the parameters of the physical measure (associated with the index) and the parameters of the risk-neutral measure (associated with the options), including the volatility and jump risk premia. The estimation is implemented using a particle filter whose efficacy is demonstrated under simulation. The computational load of this estimation method, which previously has been prohibitive, is managed by the effective use of parallel computing using graphics processing units (GPUs). The empirical results indicate that the parameters of the models are reliably estimated and consistent with values reported in previous work. In particular, both the volatility risk premium and the jump risk premium are found to be significant.

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We propose a new model for estimating the size of a population from successive catches taken during a removal experiment. The data from these experiments often have excessive variation, known as overdispersion, as compared with that predicted by the multinomial model. The new model allows catchability to vary randomly among samplings, which accounts for overdispersion. When the catchability is assumed to have a beta distribution, the likelihood function, which is refered to as beta-multinomial, is derived, and hence the maximum likelihood estimates can be evaluated. Simulations show that in the presence of extravariation in the data, the confidence intervals have been substantially underestimated in previous models (Leslie-DeLury, Moran) and that the new model provides more reliable confidence intervals. The performance of these methods was also demonstrated using two real data sets: one with overdispersion, from smallmouth bass (Micropterus dolomieu), and the other without overdispersion, from rat (Rattus rattus).

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Muscoidea is a significant dipteran clade that includes house flies (Family Muscidae), latrine flies (F. Fannidae), dung flies (F. Scathophagidae) and root maggot flies (F. Anthomyiidae). It is comprised of approximately 7000 described species. The monophyly of the Muscoidea and the precise relationships of muscoids to the closest superfamily the Oestroidea (blow flies, flesh flies etc) are both unresolved. Until now mitochondrial (mt) genomes were available for only two of the four muscoid families precluding a thorough test of phylogenetic relationships using this data source. Here we present the first two mt genomes for the families Fanniidae (Euryomma sp.) (family Fanniidae) and Anthomyiidae (Delia platura (Meigen, 1826)). We also conducted phylogenetic analyses containing of these newly sequenced mt genomes plus 15 other species representative of dipteran diversity to address the internal relationship of Muscoidea and its systematic position. Both maximum-likelihood and Bayesian analyses suggested that Muscoidea was not a monophyletic group with the relationship: (Fanniidae + Muscidae) + ((Anthomyiidae + Scathophagidae) + (Calliphoridae + Sarcophagidae)), supported by the majority of analysed datasets. This also infers that Oestroidea was paraphyletic in the majority of analyses. Divergence time estimation suggested that the earliest split within the Calyptratae, separating (Tachinidae + Oestridae) from the remaining families, occurred in the Early Eocene. The main divergence within the paraphyletic muscoidea grade was between Fanniidae + Muscidae and the lineage ((Anthomyiidae + Scathophagidae) + (Calliphoridae + Sarcophagidae)) which occurred in the Late Eocene

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Sequences of two chloroplast photosystem genes, psaA and psbB, together comprising about 3,500 bp, were obtained for all five major groups of extant seed plants and several outgroups among other vascular plants. Strongly supported, but significantly conflicting, phylogenetic signals were obtained in parsimony analyses from partitions of the data into first and second codon positions versus third positions. In the former, both genes agreed on a monophyletic gymnosperms, with Gnetales closely related to certain conifers. In the latter, Gnetales are inferred to be the sister group of all other seed plants, with gymnosperms paraphyletic. None of the data supported the modern ‘‘anthophyte hypothesis,’’ which places Gnetales as the sister group of flowering plants. A series of simulation studies were undertaken to examine the error rate for parsimony inference. Three kinds of errors were examined: random error, systematic bias (both properties of finite data sets), and statistical inconsistency owing to long-branch attraction (an asymptotic property). Parsimony reconstructions were extremely biased for third-position data for psbB. Regardless of the true underlying tree, a tree in which Gnetales are sister to all other seed plants was likely to be reconstructed for these data. None of the combinations of genes or partitions permits the anthophyte tree to be reconstructed with high probability. Simulations of progressively larger data sets indicate the existence of long-branch attraction (statistical inconsistency) for third-position psbB data if either the anthophyte tree or the gymnosperm tree is correct. This is also true for the anthophyte tree using either psaA third positions or psbB first and second positions. A factor contributing to bias and inconsistency is extremely short branches at the base of the seed plant radiation, coupled with extremely high rates in Gnetales and nonseed plant outgroups. M. J. Sanderson,* M. F. Wojciechowski,*† J.-M. Hu,* T. Sher Khan,* and S. G. Brady

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Extended spectrum β-lactamases or ESBLs, which are derived from non-ESBL precursors by point mutation of β-lactamase genes (bla), are spreading rapidly all over the world and have caused considerable problems in the treatment of infections caused by bacteria which harbour them. The mechanism of this resistance is not fully understood and a better understanding of these mechanisms might significantly impact on choosing proper diagnostic and treatment strategies. Previous work on SHV β-lactamase gene, blaSHV, has shown that only Klebsiella pneumoniae strains which contain plasmid-borne blaSHV are able to mutate to phenotypically ESBL-positive strains and there was also evidence of an increase in blaSHV copy number. Therefore, it was hypothesised that although specific point mutation is essential for acquisition of ESBL activity, it is not yet enough, and blaSHV copy number amplification is also essential for an ESBL-positive phenotype, with homologous recombination being the likely mechanism of blaSHV copy number expansion. In this study, we investigated the mutation rate of non-ESBL expressing K. pneumoniae isolates to an ESBL-positive status by using the MSS-maximum likelihood method. Our data showed that blaSHV mutation rate of a non-ESBL expressing isolate is lower than the mutation rate of the other single base changes on the chromosome, even with a plasmid-borne blaSHV gene. On the other hand, mutation rate from a low MIC ESBL-positive (≤ 8 µg/mL for cefotaxime) to high MIC ESBL-positive (≥16 µg/mL for cefotaxime) is very high. This is because only gene copy number increase is needed which is probably mediated by homologous recombination that typically takes place at a much higher frequencies than point mutations. Using a subinhibitory concentration of novobiocin, as a homologous recombination inhibitor, revealed that this is the case.

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This article describes the theoretical underpinning and development of a measurement instrument that provides teachers with a tool to observe the personal creativity characteristics of individual students. The instrument was developed by compiling a list of characteristics derived from the literature to be indicative of the personal characteristics of creative people. The list was then reduced by grouping like characteristics to 9 cognitive and dispositional traits that were considered appropriate for elementary students. The 9-item instrument was then administered in 24 classrooms to 520 Year 6 and Year 7 students. Factor analysis using maximum likelihood extraction with an oblimin rotation revealed a single factor with an eigenvalue greater than 1 and accounting for 63% of the variance. All 9 items on this factor loaded at .72 or greater. The results indicated that the Creativity Checklist has very high internal consistency and is a reliable measurement instrument (a = .93).

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Multivariate methods are required to assess the interrelationships among multiple, concurrent symptoms. We examined the conceptual and contextual appropriateness of commonly used multivariate methods for cancer symptom cluster identification. From 178 publications identified in an online database search of Medline, CINAHL, and PsycINFO, limited to articles published in English, 10 years prior to March 2007, 13 cross-sectional studies met the inclusion criteria. Conceptually, common factor analysis (FA) and hierarchical cluster analysis (HCA) are appropriate for symptom cluster identification, not principal component analysis. As a basis for new directions in symptom management, FA methods are more appropriate than HCA. Principal axis factoring or maximum likelihood factoring, the scree plot, oblique rotation, and clinical interpretation are recommended approaches to symptom cluster identification.