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 (binary and
multinomial)
and linear regression analyses were perform
10 Multivariable logistic,
multinomial,
and linear regression were used to assess a
11 tion to published data shows that use of the
multinomial approach can avoid an apparent type 1 error
12 ls under the Bayesian framework: the Poisson-
multinomial (
ASHIC-PM) model and the zero-inflated Poiss
13 SHIC-PM) model and the zero-inflated Poisson-
multinomial (
ASHIC-ZIPM) model.
14 Our tool for fast
multinomial calculations provide a simple and intuitive
15 29 samples from five data sets, we trained a
multinomial classifier to distinguish between four lung
16 caused by methodological errors, we obtained
multinomial confidence intervals (CI) for the proportion
17 or overlap weights, was more successful with
multinomial Crump and Sturmer trimming.
18 within a community should follow a zero-sum
multinomial distribution (ZSM), but this has not, so far
19 The Dirichlet-
multinomial distribution allows the analyst to calculate
20 hat K-means cluster sizes generally follow a
multinomial distribution and the failure probability of
21 produced, which closely matched a calculated
multinomial distribution based on IBC clonality.
22 nce, and viability whose parameters define a
multinomial distribution for single-spore data.
23 Under the
multinomial distribution for the read counts and a prior
24 A
multinomial distribution likelihood is constructed by co
25 In the present work, we propose a
multinomial distribution model for assessment of Ag sele
26 Using a quasi-
multinomial distribution model, our method is able to ca
27 ts from potential candidate peptides using a
multinomial distribution model.
28 framework region and CDR codons coupled with
multinomial distribution studies found no substantial ev
29 this work, we provide a method based on the
multinomial distribution that identifies signals of disp
30 The DMN distribution reduces to the
multinomial distribution when the overdispersion paramet
31 new regression model combining the Dirichlet-
multinomial distribution with recursive partitioning pro
32 Ambiguous read mapping is modeled as a
multinomial distribution, and ambiguous reads are assign
33 ted with any observed sample, against a null
multinomial distribution, using the likelihood-ratio sta
34 ribution associated with peak matches into a
multinomial distribution.
35 ations to the pattern expected from a random
multinomial distribution.
36 ations to the pattern expected from a random
multinomial distribution.
37 --using a heuristic algorithm, which matches
multinomial distributions of distinct viral variants ove
38 f internal categories, each characterized by
multinomial distributions over words (in abstracts) and
39 ther, all four are better fitted by zero-sum
multinomial distributions, characteristic of Hubbell's n
40 arity quantification method based on product
multinomial distributions, demonstrate its ability to id
41 depth for all data as a mixture of Dirichlet-
multinomial distributions, resulting in significant impr
42 The Dirichlet-
multinomial (
DMN) distribution is a fundamental model fo
43 We develop a computationally efficient
multinomial fine-mapping (MFM) approach that borrows inf
44 acteristics, including birth center, we used
multinomial generalized logit models to compare the rela
45 linear (Gaussian), binomial (logistic), and
multinomial GLMs.
46 trization of the parameters of the Dirichlet-
Multinomial likelihood.
47 Multinomial log-linear regression was performed for the
48 ng equations, latent class mixed models, and
multinomial logistic analysis, respectively.
49 Multinomial logistic and multiple linear regression mode
50 Multinomial logistic and Poisson regression models were
51 A three-covariate
multinomial logistic model derived from a triple-phase 4
52 sites in controlling splicing, we trained a
multinomial logistic model on sets of PTBP1 regulated an
53 A
multinomial logistic model was used to identify covariat
54 /asthmalike/both symptoms was evaluated by a
multinomial logistic model.
55 Logistic and
multinomial logistic models were constructed to estimate
56 Multivariate
multinomial logistic models were used to assess changes
57 omen were classified as users or nonusers in
multinomial logistic models.
58 Multinomial logistic regression (adjusted for state or j
59 Using
multinomial logistic regression (MLR), we compared the 3
60 Calibrators included ridge-penalized
multinomial logistic regression (MR) and Platt scaling b
61 from immediate graft function recipients in
multinomial logistic regression (odds ratio, 0.77; P<0.0
62 to predict HCM status was performed by using
multinomial logistic regression adjusting for age, sex,
63 We performed
multinomial logistic regression analyses adjusted for so
64 Multinomial logistic regression analyses indicated that
65 Multinomial logistic regression analyses were conducted
66 loss of sexual activity were assessed using
multinomial logistic regression analyses.
67 Multinomial logistic regression analysis identified peri
68 We performed
multinomial logistic regression analysis to assess the w
69 We performed a multivariable
multinomial logistic regression analysis to estimate odd
70 We performed a
multinomial logistic regression analysis to estimate the
71 We used
multinomial logistic regression analysis to examine fact
72 Multinomial logistic regression analysis was performed t
73 Multinomial logistic regression analysis was used to fit
74 positively charged amino acids, according to
multinomial logistic regression analysis.
75 e development of each asthma phenotype using
multinomial logistic regression analysis.
76 Multinomial logistic regression analyzed the relationshi
77 Multinomial logistic regression and linear regression we
78 maging biomarkers with OI was examined using
multinomial logistic regression and simple linear regres
79 ample, analytic methods such as quantile and
multinomial logistic regression can describe the effects
80 Data were analyzed using
multinomial logistic regression controlling for age, gen
81 ion of glucose tolerance were assessed using
multinomial logistic regression corrected for familial c
82 Multinomial logistic regression estimated AHOs odds rati
83 Multinomial logistic regression estimated separate ORs f
84 alized US adults aged 18 years or older, and
multinomial logistic regression examines whether variabl
85 Multinomial logistic regression for clustered data indic
86 ear regression for continuous phenotypes and
multinomial logistic regression for skeletal malocclusio
87 However, adjusted
multinomial logistic regression indicates each unit incr
88 Analyses included a
multinomial logistic regression model for early- and lat
89 A conservative penalized
multinomial logistic regression model identified 14 vari
90 A
multinomial logistic regression model showed that optoac
91 uate vital registration system; we applied a
multinomial logistic regression model to vital registrat
92 A
multinomial logistic regression model was used to differ
93 A
multinomial logistic regression model was used to infer
94 A person-time
multinomial logistic regression model was used to simult
95 tes were analyzed using a first-order Markov
multinomial logistic regression model with 11 different
96 Our method is based on the
multinomial logistic regression model with a tree-guided
97 sed levels of HBD-2 (Pearson correlation and
multinomial logistic regression model).
98 In the adjusted
multinomial logistic regression model, a serum bicarbona
99 high-incidence) as dependent variables in a
multinomial logistic regression model.
100 R: 0.85; 95% CI: 0.54, 1.34) in the adjusted
multinomial logistic regression model.
101 on behavior were analyzed in a multivariable
multinomial logistic regression model.
102 iles of comorbid symptoms, and multivariable
multinomial logistic regression modeling examined associ
103 Multinomial logistic regression modeling indicated that
104 02) but not after 6- and 9-y of follow-up in
multinomial logistic regression models adjusted for base
105 We used sex-specific linear and
multinomial logistic regression models adjusted for demo
106 Using
multinomial logistic regression models adjusted for pati
107 sion models and as a categorical variable in
multinomial logistic regression models adjusted for sex,
108 s of BMI and WHR with DR were assessed using
multinomial logistic regression models adjusting for age
109 Data were analyzed with logistic and
multinomial logistic regression models controlling for d
110 Multinomial logistic regression models estimated the ass
111 Multinomial logistic regression models examined the asso
112 Results from adjusted
multinomial logistic regression models indicated that re
113 We used adjusted
multinomial logistic regression models to estimate odds
114 Multinomial logistic regression models were fit to deter
115 Two
multinomial logistic regression models were used to anal
116 Multinomial logistic regression models were used to asse
117 Multinomial logistic regression models were used to comp
118 Multinomial logistic regression models were used to esta
119 Multinomial logistic regression models were used to exam
120 Multinomial logistic regression models were used to exam
121 esonance imaging using linear regression and
multinomial logistic regression models.
122 imated through relative risk ratios (RRR) by
multinomial logistic regression models.
123 These were used as covariates in 10
multinomial logistic regression models.
124 atus and genetic ancestry using logistic and
multinomial logistic regression models.
125 ffect of baseline factors was assessed using
multinomial logistic regression models.
126 Logistic and
multinomial logistic regression of outcomes, estrogen re
127 Logistic and
multinomial logistic regression of the data were conduct
128 Multinomial logistic regression provides an attractive f
129 Multinomial logistic regression revealed that being with
130 Multinomial logistic regression showed that country, age
131 Fixed effects
multinomial logistic regression showed that shortened di
132 We used
multinomial logistic regression to assess whether charac
133 We use
multinomial logistic regression to correlate the yearly
134 We used
multinomial logistic regression to estimate unadjusted a
135 We used
multinomial logistic regression to evaluate associations
136 We used
multinomial logistic regression to evaluate the relation
137 g hemodialysis in the United States, we used
multinomial logistic regression to evaluate whether prio
138 We used
multinomial logistic regression to generate covariates o
139 We used
multinomial logistic regression to identify baseline fac
140 a to identify linear growth trajectories and
multinomial logistic regression to identify covariates t
141 We also used
multinomial logistic regression to identify factors asso
142 Multinomial logistic regression was performed to compare
143 f distress and depression were examined, and
multinomial logistic regression was performed.
144 A
multinomial logistic regression was then used to predict
145 Multinomial logistic regression was used to ascertain fa
146 Multinomial logistic regression was used to assess the i
147 Multinomial logistic regression was used to determine as
148 Multinomial logistic regression was used to determine de
149 Multinomial logistic regression was used to determine th
150 Logistic and
multinomial logistic regression was used to determine th
151 Weighted
multinomial logistic regression was used to estimate ass
152 Multinomial logistic regression was used to estimate ORs
153 Multinomial logistic regression was used to estimate the
154 Multivariate
multinomial logistic regression was used to estimate the
155 Multinomial logistic regression was used to evaluate fac
156 Multinomial logistic regression was used to evaluate the
157 Multinomial logistic regression was used to examine fact
158 Multinomial logistic regression was used to examine the
159 Multinomial logistic regression was used to identify bas
160 Multinomial logistic regression was used to identify pot
161 Multinomial logistic regression was used to identify pot
162 Multinomial logistic regression was used to investigate
163 Multinomial logistic regression was used to report unadj
164 Multinomial logistic regression was used to test the ass
165 Penalized
multinomial logistic regression was utilized to create a
166 ANOVA comparison and adjusted
multinomial logistic regression were used to evaluate cl
167 Descriptive statistics and
multinomial logistic regression were used to explore mat
168 were estimated in a hip-based analysis using
multinomial logistic regression with adjustment for age,
169 Risk was assessed through multivariable and
multinomial logistic regression with adjustment for rele
170 equate vital registration; we used a similar
multinomial logistic regression with verbal autopsy data
171 Hospital characteristics (using
multinomial logistic regression) and survival (using Cox
172 vents after starting treatment (P = .005, by
multinomial logistic regression) but not death.
173 dds of increased out-of-pocket costs (survey
multinomial logistic regression, adjusted odds ratios [O
174 Subsequent
multinomial logistic regression, MultiPhen and Random Fo
175 sing 2-level logistic regression and 2-level
multinomial logistic regression, respectively.
176 Using
multinomial logistic regression, risk ratios of > +0.5 d
177 Using
multinomial logistic regression, the authors found that
178 Using
multinomial logistic regression, we examined the associa
179 Using weighted
multinomial logistic regression, we modeled each barrier
180 Risk factors were modelled using
multinomial logistic regression.
181 tures of the 3 organisms were compared using
multinomial logistic regression.
182 r of siblings and AMD were assessed by using
multinomial logistic regression.
183 tization and risk factors were studied using
multinomial logistic regression.
184 valuated the association between the 2 using
multinomial logistic regression.
185 orical obesity status was predicted by using
multinomial logistic regression.
186 nse and EPS classification was identified by
multinomial logistic regression.
187 ent strategy were evaluated in multivariable
multinomial logistic regression.
188 TW patterns were assessed using multivariate
multinomial logistic regression.
189 GERD, non-GERD, or EoE) were estimated using
multinomial logistic regression.
190 nitive change categories were examined using
multinomial logistic regression.
191 se outcomes were then tested with the use of
multinomial logistic regression.An ED, HF, and LFD dieta
192 Multinomial logistic regressions and propensity score ma
193 cause-specific mortality fractions applying
multinomial logistic regressions using adequate VR for l
194 oncentrations (>/=14 ng/L) using Poisson and
multinomial logistic regressions, respectively.
195 ors and clinical outcome were analyzed using
multinomial logistic regressions.
196 fspring allergic disease were estimated with
multinomial logistic regressions.
197 Multinomial logistical regression analysis was used to i
198 Multinomial logit analysis was used to examine the assoc
199 A
multinomial logit and a latent class logit model was use
200 We used a Bayesian
multinomial logit Gaussian process model to produce esti
201 neralised linear latent and mixed model with
multinomial logit link to adjust for clustering within h
202 Multinomial logit modeling also accounts for the impact
203 hness or wildflower viewing utility based on
multinomial logit models of revealed preferences, rankin
204 We used conditional
multinomial logit models to examine differences in hospi
205 Data were analyzed by
multinomial logit models.
206 Multivariate
multinomial logit regression investigated the associatio
207 Multinomial logit regression was used to examine the inc
208 A best-worst scaling survey, analyzed by
multinomial-
logit models, was used to calculate normaliz
209 We propose simple
multinomial methods, including generalized principal com
210 d the application of prior construction to a
multinomial mixture model when labels are unknown, which
211 Dirichlet
multinomial mixture modeling, Markov chain analysis, and
212 he course of 12-18 months, we used Dirichlet
multinomial mixture models to partition the data into co
213 se multivariate methods based on Poisson and
multinomial mixture models to segment SIMS images into c
214 Dirichlet
multinomial mixtures identified four compositionally dis
215 ension of optimal Bayesian classification to
multinomial mixtures where data sets are both small and
216 Multinomial model and Focused binomial test demonstrated
217 The
multinomial model combines the database search results a
218 We suggest the use of the
multinomial model for all future analysis of Ag selectio
219 of random matches, we employ a marginalized
multinomial model for small values of cross-correlation
220 The
multinomial model is derived as a standardized Poisson m
221 riance against mean conductance by fitting a
multinomial model that incorporated both spatial variati
222 power calculations make use of the Dirichlet-
Multinomial model to describe and generate abundances.
223 y model used for spectratype analysis is the
multinomial model with n, the total number of counts, in
224 between the evolutionary tree model and the
multinomial model with that of marginalized tests applie
225 Among nine predictor variables included in a
multinomial model, only female gender and days in ICU wi
226 ach that ensures that one remains within the
multinomial model.
227 This extra information is then used in a
multinomial modeling approach for estimating parent-of-o
228 sociation studies using either log-linear or
multinomial modeling approaches.
229 Log-linear and
multinomial modeling offer a flexible framework for gene
230 es of the eight groups were determined using
multinomial models combining data from 435 individuals w
231 The results from our
multinomial models suggest that A(+)N(-) and A(+)N(+) we
232 RRD repair and used multilevel mixed effects
multinomial models to characterize variation in repair t
233 nt MicroBVS, an R package for Dirichlet-tree
multinomial models with Bayesian variable selection, for
234 ependent models, two-hypothesis binomial and
multinomial models, which use the hypergeometric probabi
235 and sex by including these covariates in the
multinomial models.
236 sity score-adjusted and probability-weighted
multinomial multivariable logistic regression was used t
237 The best classification system is a
multinomial naive Bayes classifier trained on manually a
238 periods were associated with atopic asthma (
multinomial odds ratio (MOR) = 2.79, 95% confidence inte
239 : 1.21, 1.87) but less atopy alone (adjusted
multinomial odds ratio = 0.80, 95% confidence interval:
240 sition (SEP) had more asthma alone (adjusted
multinomial odds ratio = 1.50, 95% confidence interval:
241 o introduce solids at age 4 months (adjusted
multinomial odds ratio [aMOR], 1.21; 95% CI, 1.02-1.45;
242 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
243 We used
multinomial or logistic regression to explore the associ
244 model specification for predicting exposure (
multinomial or logistic regression) and characterization
245 Multinomial ordinal logistic regression confirmed that i
246 risk factor affects certain categories of a
multinomial outcome but not others, outcome heterogeneit
247 used across various scientific disciplines:
multinomial,
Poisson, hypergeometric, and Bernoulli prod
248 Instead, we use the
multinomial-
Poisson hierarchy model and demonstrate that
249 nal affective disorder was compared by using
multinomial probability distribution tests.
250 A protein identification probability is the
multinomial probability of observing the given set of pe
251 Results were analyzed with adjusted
multinomial propensity score.
252 In conclusion, our proposed definitions of
multinomial PS trimming methods were beneficial within o
253 ized the trimming definitions by considering
multinomial PSs, one for each treatment, and proved that
254 ous year at ages 16 years and 21 years), and
multinomial regression (lifetime self-harm with and with
255 ts who reported having used alcohol, Cox and
multinomial regression analyses were used to assess the
256 Univariate and multivariable and ordinal
multinomial regression analyses were used to test associ
257 A
multinomial regression analysis was performed to identif
258 Multinomial regression analysis was used to determine th
259 onutrients was investigated using linear and
multinomial regression analysis.
260 Statistical significance was assessed by
multinomial regression and multiple linear regression an
261 d with detection of G. vaginalis clades, and
multinomial regression assessed factors associated with
262 Multinomial regression demonstrated that lung function w
263 Multinomial regression evaluated the effects of graphica
264 We performed Bayesian
multinomial regression in 16 664 stroke cases and 32 792
265 manuscript, we propose a Bayesian Dirichlet-
Multinomial regression model which uses spike-and-slab p
266 ed from 2006 to 2010 and analysed by using a
multinomial regression model.
267 s with rectal viral load were explored using
multinomial regression modeling.
268 We used
multinomial regression models to evaluate the effect of
269 Multinomial regression models were used to estimate risk
270 positive [A(+)N(+)]) cross-sectionally using
multinomial regression models.
271 Time trends were analyzed with
multinomial regression models.
272 Multinomial regression showed that participants with kne
273 e derived from parameter estimates (beta) of
multinomial regression stratified according to study par
274 Using
multinomial regression the five variables with the large
275 We used log-binomial and
multinomial regression to calculate adjusted relative ri
276 We used
multinomial regression to identify frailty correlates.
277 of carbon monoxide on platinum, we implement
multinomial regression via neural network ensembles to l
278 Multinomial regression was used to ascertain which clima
279 Weighted
multinomial regression was used to assess the relationsh
280 Multinomial regression was used to assess which covariat
281 pontaneous survival [SS]) were obtained with
multinomial regression, and observed-to-expected ratios
282 ion and asthma/COPD/ACOS were examined using
multinomial regression.
283 riate analysis and multivariate logistic and
multinomial regression.
284 rweight and obesity were estimated by use of
multinomial regression.
285 re assessed using multivariable logistic and
multinomial regression.
286 We fitted
multinomial regressions for each state and subgroup to e
287 d/or type of alteration, follow binomial and
multinomial sampling distributions, respectively.
288 d signature fitting, by explicitly factoring
multinomial sampling into the objective function.
289 model yields the Wright-Fisher model (i.e.,
multinomial sampling of genes) if and only if the viabil
290 ling of genotypes generally does not lead to
multinomial sampling of genes.
291 Thus,
multinomial sampling of genotypes generally does not lea
292 Three different derivations of models with
multinomial sampling of genotypes in a finite population
293 negative controls, we show UMI counts follow
multinomial sampling with no zero inflation.
294 Utilizing
multinomial statistics, we found that attraction rates t
295 Multinomial Sturmer and Walker trimming were more succes
296 applies conventional methods appropriate for
multinomial tables to statistics calculated from EBQP ta
297 kidney injury and daily mental status using
multinomial transition models adjusting for demographics
298 hen examined the performance of the proposed
multinomial trimming methods in the setting of 3 treatme
299 the generalized logit model for binomial and
multinomial variables adjusted for age, sex, education,
300 deviations were determined by estimating the
multinomial variance associated with each element of the