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1 area and estimated genetic ancestry using a maximum likelihood method.
2 We develop a penalized maximum likelihood method.
3 adation rate constants were estimated by the maximum likelihood method.
4 ed to cause problems when analyzed using the maximum likelihood method.
5 is run over bootstrap trees estimated by the maximum likelihood method.
6 Phylogenetic trees were generated using the maximum likelihood method.
7 s used to determine odds ratios (ORs) by the maximum likelihood method.
8 miliality in nondiabetic Pima Indians with a maximum-likelihood method.
9 This improves on a previous maximum-likelihood method.
10 r-joining analyses and were confirmed by the maximum-likelihood method.
11 ical model and algorithm using the classical maximum-likelihood method.
12 ss Monogononta, employing distance based and maximum likelihood methods.
13 ed with the results of maximum parsimony and maximum likelihood methods.
14 ype frequencies were estimated with standard maximum likelihood methods.
15 mous substitutions with multiple codon-based maximum likelihood methods.
16 umventing the difficulties that are faced by maximum likelihood methods.
17 data, an improvement over more commonly used maximum likelihood methods.
18 American milkweed species (Asclepias), using maximum likelihood methods.
19 of normal-model multiple imputation (MI) and maximum likelihood methods.
20 ol-based deworming programme in Uganda using maximum likelihood methods.
21 ents and with a precision similar to that of maximum likelihood methods.
22 g, minimum evolution, maximum parsimony, and maximum likelihood methods.
23 tes, and Rodentia using both approximate and maximum-likelihood methods.
24 with distance-based, maximum-parsimony, and maximum-likelihood methods.
25 ur major predictions of defense theory using maximum-likelihood methods.
26 n be characterized more accurately than with maximum-likelihood methods.
27 multiple traits, most of which are based on maximum-likelihood methods.
28 t-matching methods fit the tails better than maximum-likelihood methods.
29 ucted for these sequences using Bayesian and maximum-likelihood methods.
32 ymous (dN/dS) analyses were performed with a maximum likelihood method and an approximate method for
33 as correction approaches-the Firth penalized maximum likelihood method and Cordeiro and McCullagh's b
34 es finite and less biased estimates than the maximum likelihood method and Cordeiro and McCullagh's m
36 ates from three FST estimators, a coalescent maximum-likelihood method and Bayesian recent migration
37 gated placental phylogeny using Bayesian and maximum-likelihood methods and a 16.4-kilobase molecular
38 ing ancestral sequences through Bayesian and Maximum Likelihood methods, and/or by resurrecting ances
40 ata can be used for parameter estimation via maximum likelihood methods as long as the way in which t
41 tional on the gene tree, were made using new maximum likelihood methods assuming a coalescent model.
42 III were tested for positive selection using maximum likelihood methods based on models of codon subs
44 we find a significant correlation if we use maximum-likelihood methods but not if we use algorithmic
47 Recent research suggests that model-based, maximum likelihood methods can improve these analyses.
50 ially matches the conjectured performance of maximum likelihood methods--challenging the idea that su
52 simony method of Suzuki and Gojobori and the maximum likelihood method developed from the work of Nie
54 imony, neighbor-joining, Fitch-Margolish, or maximum likelihood methods failed to show the clustering
55 genetic tree in the SG method, and present a maximum likelihood method for detecting positive selecti
58 ction using neighbor-joining, parsimony, and maximum likelihood methods for 23S rRNA gene sequence da
60 We have developed weighted parsimony and maximum likelihood methods for inferring gain and loss e
61 Computer simulations are used to evaluate maximum likelihood methods for inferring male fertility
62 rce code for phylogenetic analysis using the maximum likelihood methods for parallel execution on mul
66 incorporate disease prevalence and develop a maximum-likelihood method for estimating L that uses the
73 imating these parameters using parsimony and maximum-likelihood methods for each of the random topolo
77 ns (GEE), as a potential alternative to full maximum-likelihood methods, for performing segregation a
78 ance based methods though not as accurate as maximum likelihood methods from good quality multiple se
79 a wide range of statistical techniques (e.g. maximum likelihood methods, generalized additive models,
80 nalysis of the HLA data demonstrate that the maximum likelihood method has good power and accuracy in
83 bination of distance, maximum parsimony, and maximum likelihood methods indicate that heliobacteria a
87 rogression of the tumor and that the partial maximum likelihood method of Greenman et al. (2012) can
89 nt cytotoxicity prior to cleavage, we used a maximum likelihood method of reconstructing ancestral st
92 on of seven different substitution models by maximum-likelihood methods revealed that the fit of the
93 ds-comparing alternative distributions using maximum likelihood methods-showed the strongest support
94 ing maximum parsimony, neighbor-joining, and maximum likelihood methods strongly support a D. yakuba-
95 st for various factors that typically affect maximum likelihood methods, such as number of taxa, leve
96 this mechanism, were fitted to data using a maximum likelihood method that uses the Hawkes-Jalali-Co
97 trices, methods based on Markov triples, and maximum likelihood methods that infer the substitution p
98 ucted a genome scan of diabetes status using maximum likelihood methods that model affection status b
101 this problem, we developed Recon, a modified maximum-likelihood method that outputs the overall diver
102 old model fitting using the full information maximum likelihood method to estimate genetic and enviro
104 st year and we employed the full information maximum likelihood method to handle missing data on four
105 case-control analysis employing the marginal maximum likelihood method to infer genotypes of relative
107 hylogenetic tree inference with Bayesian and maximum likelihood methods to elucidate the pattern of e
112 ly ascertained, linked families, by use of a maximum-likelihood method to incorporate both cancer-inc
114 ng exons in 12 primate species and, by using maximum-likelihood methods to determine sites under posi
115 study we reexamine those relationships using maximum-likelihood methods to estimate substitution rate
116 ion of the NBS-LRR domain architecture, used maximum-likelihood methods to infer a phylogeny of the N
118 ty-sensitive method, instead of the standard maximum-likelihood method, to maximize directly the expe
119 tigated in comparison with the mixture model maximum likelihood method under high heritabilities, dom
120 ds the work of Weir & Cockerham by employing maximum likelihood methods under the assumption that all
122 e specific energy values are determined by a maximum likelihood method using examples from in vitro r
132 Our approach is based on a probabilistic maximum likelihood method, which is necessary to disenta
134 ated alignments were evaluated thoroughly by maximum-likelihood methods, with each of the three herpe
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