1 The MASPIC (
Multinomial Algorithm for Spectral Profile-based Intensi
2 Each dataset is modelled using a Dirichlet-
multinomial allocation (DMA) mixture model, with depende
3 o, a program that implements a wide range of
multinomial analyses in a single fast package that is de
4 With use of Dirichlet
multinomial analysis and mixed models to account for rep
5 We used linear and ordered
multinomial analysis with a country fixed effect to obta
6 Side-by-side application of
multinomial and binomial models on 86 previously establi
7 osystems QSTAR fits well to a combination of
multinomial and Poisson model with detector dead-time co
8 Logistic regression (binary and
multinomial)
and analysis of covariance were used to exa
9 Multivariable logistic,
multinomial,
and linear regression were used to assess a
10 tion to published data shows that use of the
multinomial approach can avoid an apparent type 1 error
11 29 samples from five data sets, we trained a
multinomial classifier to distinguish between four lung
12 caused by methodological errors, we obtained
multinomial confidence intervals (CI) for the proportion
13 within a community should follow a zero-sum
multinomial distribution (ZSM), but this has not, so far
14 The Dirichlet-
multinomial distribution allows the analyst to calculate
15 hat K-means cluster sizes generally follow a
multinomial distribution and the failure probability of
16 produced, which closely matched a calculated
multinomial distribution based on IBC clonality.
17 nce, and viability whose parameters define a
multinomial distribution for single-spore data.
18 A
multinomial distribution likelihood is constructed by co
19 In the present work, we propose a
multinomial distribution model for assessment of Ag sele
20 Using a quasi-
multinomial distribution model, our method is able to ca
21 ts from potential candidate peptides using a
multinomial distribution model.
22 framework region and CDR codons coupled with
multinomial distribution studies found no substantial ev
23 this work, we provide a method based on the
multinomial distribution that identifies signals of disp
24 The DMN distribution reduces to the
multinomial distribution when the overdispersion paramet
25 Ambiguous read mapping is modeled as a
multinomial distribution, and ambiguous reads are assign
26 Hence, instead of the conventional
multinomial distribution, these tables have the empirica
27 ted with any observed sample, against a null
multinomial distribution, using the likelihood-ratio sta
28 ribution associated with peak matches into a
multinomial distribution.
29 ations to the pattern expected from a random
multinomial distribution.
30 ations to the pattern expected from a random
multinomial distribution.
31 --using a heuristic algorithm, which matches
multinomial distributions of distinct viral variants ove
32 f internal categories, each characterized by
multinomial distributions over words (in abstracts) and
33 ther, all four are better fitted by zero-sum
multinomial distributions, characteristic of Hubbell's n
34 arity quantification method based on product
multinomial distributions, demonstrate its ability to id
35 depth for all data as a mixture of Dirichlet-
multinomial distributions, resulting in significant impr
36 The Dirichlet-
multinomial (
DMN) distribution is a fundamental model fo
37 acteristics, including birth center, we used
multinomial generalized logit models to compare the rela
38 trization of the parameters of the Dirichlet-
Multinomial likelihood.
39 Multinomial log-linear regression was performed for the
40 ng equations, latent class mixed models, and
multinomial logistic analysis, respectively.
41 Multinomial logistic and Poisson regression models were
42 A three-covariate
multinomial logistic model derived from a triple-phase 4
43 sites in controlling splicing, we trained a
multinomial logistic model on sets of PTBP1 regulated an
44 /asthmalike/both symptoms was evaluated by a
multinomial logistic model.
45 Logistic and
multinomial logistic models were constructed to estimate
46 Multivariate
multinomial logistic models were used to assess changes
47 omen were classified as users or nonusers in
multinomial logistic models.
48 from immediate graft function recipients in
multinomial logistic regression (odds ratio, 0.77; P<0.0
49 We performed
multinomial logistic regression analyses adjusted for so
50 Multinomial logistic regression analyses indicated that
51 loss of sexual activity were assessed using
multinomial logistic regression analyses.
52 Multinomial logistic regression analysis identified peri
53 We performed
multinomial logistic regression analysis to assess the w
54 We performed a multivariable
multinomial logistic regression analysis to estimate odd
55 We performed a
multinomial logistic regression analysis to estimate the
56 Multinomial logistic regression analysis was performed t
57 positively charged amino acids, according to
multinomial logistic regression analysis.
58 e development of each asthma phenotype using
multinomial logistic regression analysis.
59 maging biomarkers with OI was examined using
multinomial logistic regression and simple linear regres
60 ample, analytic methods such as quantile and
multinomial logistic regression can describe the effects
61 Data were analyzed using
multinomial logistic regression controlling for age, gen
62 ion of glucose tolerance were assessed using
multinomial logistic regression corrected for familial c
63 Multinomial logistic regression estimated AHOs odds rati
64 Multinomial logistic regression estimated separate ORs f
65 alized US adults aged 18 years or older, and
multinomial logistic regression examines whether variabl
66 Multinomial logistic regression for clustered data indic
67 ear regression for continuous phenotypes and
multinomial logistic regression for skeletal malocclusio
68 Analyses included a
multinomial logistic regression model for early- and lat
69 A conservative penalized
multinomial logistic regression model identified 14 vari
70 uate vital registration system; we applied a
multinomial logistic regression model to vital registrat
71 A
multinomial logistic regression model was used to differ
72 A
multinomial logistic regression model was used to infer
73 A person-time
multinomial logistic regression model was used to simult
74 tes were analyzed using a first-order Markov
multinomial logistic regression model with 11 different
75 Our method is based on the
multinomial logistic regression model with a tree-guided
76 sed levels of HBD-2 (Pearson correlation and
multinomial logistic regression model).
77 In the adjusted
multinomial logistic regression model, a serum bicarbona
78 on behavior were analyzed in a multivariable
multinomial logistic regression model.
79 high-incidence) as dependent variables in a
multinomial logistic regression model.
80 R: 0.85; 95% CI: 0.54, 1.34) in the adjusted
multinomial logistic regression model.
81 iles of comorbid symptoms, and multivariable
multinomial logistic regression modeling examined associ
82 02) but not after 6- and 9-y of follow-up in
multinomial logistic regression models adjusted for base
83 We used sex-specific linear and
multinomial logistic regression models adjusted for demo
84 Using
multinomial logistic regression models adjusted for pati
85 sion models and as a categorical variable in
multinomial logistic regression models adjusted for sex,
86 s of BMI and WHR with DR were assessed using
multinomial logistic regression models adjusting for age
87 Data were analyzed with logistic and
multinomial logistic regression models controlling for d
88 Multinomial logistic regression models estimated the ass
89 Multinomial logistic regression models examined the asso
90 Results from adjusted
multinomial logistic regression models indicated that re
91 We used adjusted
multinomial logistic regression models to estimate odds
92 Two
multinomial logistic regression models were used to anal
93 Multinomial logistic regression models were used to asse
94 Multinomial logistic regression models were used to comp
95 Multinomial logistic regression models were used to exam
96 Multinomial logistic regression models were used to exam
97 These were used as covariates in 10
multinomial logistic regression models.
98 ffect of baseline factors was assessed using
multinomial logistic regression models.
99 esonance imaging using linear regression and
multinomial logistic regression models.
100 imated through relative risk ratios (RRR) by
multinomial logistic regression models.
101 Logistic and
multinomial logistic regression of outcomes, estrogen re
102 Logistic and
multinomial logistic regression of the data were conduct
103 Multinomial logistic regression provides an attractive f
104 Multinomial logistic regression revealed that being with
105 Multinomial logistic regression showed that country, age
106 We used
multinomial logistic regression to assess whether charac
107 We used
multinomial logistic regression to estimate unadjusted a
108 We used
multinomial logistic regression to evaluate the relation
109 a to identify linear growth trajectories and
multinomial logistic regression to identify covariates t
110 Multinomial logistic regression was performed to compare
111 f distress and depression were examined, and
multinomial logistic regression was performed.
112 A
multinomial logistic regression was then used to predict
113 Multinomial logistic regression was used to ascertain fa
114 Multinomial logistic regression was used to assess the i
115 Multinomial logistic regression was used to determine as
116 Multinomial logistic regression was used to determine de
117 Logistic and
multinomial logistic regression was used to determine th
118 Multinomial logistic regression was used to determine th
119 Multinomial logistic regression was used to estimate the
120 Multinomial logistic regression was used to evaluate fac
121 Multinomial logistic regression was used to evaluate the
122 Multinomial logistic regression was used to examine fact
123 Multinomial logistic regression was used to examine the
124 Multinomial logistic regression was used to identify bas
125 Multinomial logistic regression was used to identify pot
126 Multinomial logistic regression was used to identify pot
127 Multinomial logistic regression was used to investigate
128 Multinomial logistic regression was used to report unadj
129 Multinomial logistic regression was used to test the ass
130 Penalized
multinomial logistic regression was utilized to create a
131 Descriptive statistics and
multinomial logistic regression were used to explore mat
132 were estimated in a hip-based analysis using
multinomial logistic regression with adjustment for age,
133 Risk was assessed through multivariable and
multinomial logistic regression with adjustment for rele
134 equate vital registration; we used a similar
multinomial logistic regression with verbal autopsy data
135 Hospital characteristics (using
multinomial logistic regression) and survival (using Cox
136 dds of increased out-of-pocket costs (survey
multinomial logistic regression, adjusted odds ratios [O
137 Subsequent
multinomial logistic regression, MultiPhen and Random Fo
138 Using
multinomial logistic regression, the authors found that
139 Using
multinomial logistic regression, we examined the associa
140 Using weighted
multinomial logistic regression, we modeled each barrier
141 tures of the 3 organisms were compared using
multinomial logistic regression.
142 r of siblings and AMD were assessed by using
multinomial logistic regression.
143 nitive change categories were examined using
multinomial logistic regression.
144 tization and risk factors were studied using
multinomial logistic regression.
145 valuated the association between the 2 using
multinomial logistic regression.
146 orical obesity status was predicted by using
multinomial logistic regression.
147 nse and EPS classification was identified by
multinomial logistic regression.
148 Risk factors were modelled using
multinomial logistic regression.
149 se outcomes were then tested with the use of
multinomial logistic regression.An ED, HF, and LFD dieta
150 Multinomial logistic regressions and propensity score ma
151 cause-specific mortality fractions applying
multinomial logistic regressions using adequate VR for l
152 oncentrations (>/=14 ng/L) using Poisson and
multinomial logistic regressions, respectively.
153 fspring allergic disease were estimated with
multinomial logistic regressions.
154 ors and clinical outcome were analyzed using
multinomial logistic regressions.
155 Multinomial logistical regression analysis was used to i
156 Multinomial logit analysis was used to examine the assoc
157 neralised linear latent and mixed model with
multinomial logit link to adjust for clustering within h
158 Multinomial logit modeling also accounts for the impact
159 hness or wildflower viewing utility based on
multinomial logit models of revealed preferences, rankin
160 We used conditional
multinomial logit models to examine differences in hospi
161 Data were analyzed by
multinomial logit models.
162 Multinomial logit regression was used to examine the inc
163 A best-worst scaling survey, analyzed by
multinomial-
logit models, was used to calculate normaliz
164 d the application of prior construction to a
multinomial mixture model when labels are unknown, which
165 Dirichlet
multinomial mixture modeling, Markov chain analysis, and
166 he course of 12-18 months, we used Dirichlet
multinomial mixture models to partition the data into co
167 se multivariate methods based on Poisson and
multinomial mixture models to segment SIMS images into c
168 ension of optimal Bayesian classification to
multinomial mixtures where data sets are both small and
169 Multinomial model and Focused binomial test demonstrated
170 The
multinomial model combines the database search results a
171 We suggest the use of the
multinomial model for all future analysis of Ag selectio
172 of random matches, we employ a marginalized
multinomial model for small values of cross-correlation
173 The
multinomial model is derived as a standardized Poisson m
174 riance against mean conductance by fitting a
multinomial model that incorporated both spatial variati
175 power calculations make use of the Dirichlet-
Multinomial model to describe and generate abundances.
176 y model used for spectratype analysis is the
multinomial model with n, the total number of counts, in
177 between the evolutionary tree model and the
multinomial model with that of marginalized tests applie
178 ach that ensures that one remains within the
multinomial model.
179 This extra information is then used in a
multinomial modeling approach for estimating parent-of-o
180 es of the eight groups were determined using
multinomial models combining data from 435 individuals w
181 The results from our
multinomial models suggest that A(+)N(-) and A(+)N(+) we
182 ependent models, two-hypothesis binomial and
multinomial models, which use the hypergeometric probabi
183 and sex by including these covariates in the
multinomial models.
184 The best classification system is a
multinomial naive Bayes classifier trained on manually a
185 periods were associated with atopic asthma (
multinomial odds ratio (MOR) = 2.79, 95% confidence inte
186 : 1.21, 1.87) but less atopy alone (adjusted
multinomial odds ratio = 0.80, 95% confidence interval:
187 sition (SEP) had more asthma alone (adjusted
multinomial odds ratio = 1.50, 95% confidence interval:
188 o introduce solids at age 4 months (adjusted
multinomial odds ratio [aMOR], 1.21; 95% CI, 1.02-1.45;
189 by 22 years was higher in men than in women (
multinomial odds ratio [M-OR] 2.0, 95% CI 1.2-3.2, p=0.0
190 model specification for predicting exposure (
multinomial or logistic regression) and characterization
191 Multinomial ordinal logistic regression confirmed that i
192 risk factor affects certain categories of a
multinomial outcome but not others, outcome heterogeneit
193 used across various scientific disciplines:
multinomial,
Poisson, hypergeometric, and Bernoulli prod
194 Instead, we use the
multinomial-
Poisson hierarchy model and demonstrate that
195 nal affective disorder was compared by using
multinomial probability distribution tests.
196 A protein identification probability is the
multinomial probability of observing the given set of pe
197 Results were analyzed with adjusted
multinomial propensity score.
198 ts who reported having used alcohol, Cox and
multinomial regression analyses were used to assess the
199 Univariate and multivariable and ordinal
multinomial regression analyses were used to test associ
200 A
multinomial regression analysis was performed to identif
201 Multinomial regression analysis was used to determine th
202 onutrients was investigated using linear and
multinomial regression analysis.
203 Multinomial regression demonstrated that lung function w
204 manuscript, we propose a Bayesian Dirichlet-
Multinomial regression model which uses spike-and-slab p
205 ed from 2006 to 2010 and analysed by using a
multinomial regression model.
206 s with rectal viral load were explored using
multinomial regression modeling.
207 Time trends were analyzed with
multinomial regression models.
208 positive [A(+)N(+)]) cross-sectionally using
multinomial regression models.
209 Using
multinomial regression the five variables with the large
210 We used log-binomial and
multinomial regression to calculate adjusted relative ri
211 We used
multinomial regression to identify frailty correlates.
212 Multinomial regression was used to ascertain which clima
213 Weighted
multinomial regression was used to assess the relationsh
214 riate analysis and multivariate logistic and
multinomial regression.
215 rweight and obesity were estimated by use of
multinomial regression.
216 ion and asthma/COPD/ACOS were examined using
multinomial regression.
217 d/or type of alteration, follow binomial and
multinomial sampling distributions, respectively.
218 s always) a good approximation for genotypic
multinomial sampling in large populations.
219 model yields the Wright-Fisher model (i.e.,
multinomial sampling of genes) if and only if the viabil
220 ling of genotypes generally does not lead to
multinomial sampling of genes.
221 Thus,
multinomial sampling of genotypes generally does not lea
222 Three different derivations of models with
multinomial sampling of genotypes in a finite population
223 Utilizing
multinomial statistics, we found that attraction rates t
224 applies conventional methods appropriate for
multinomial tables to statistics calculated from EBQP ta
225 kidney injury and daily mental status using
multinomial transition models adjusting for demographics
226 This hierarchical model consists of two
multinomial trials: one of the sampling process of the p
227 deviations were determined by estimating the
multinomial variance associated with each element of the