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You searched for: Subject "normal distribution" Remove constraint Subject: "normal distribution" Journal Biometrics Remove constraint Journal: Biometrics Subject algorithms Remove constraint Subject: algorithms
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1. Flexible parametric approach to classical measurement error variance estimation without auxiliary data

Author:
Bertrand, Aurélie; Van Keilegom, Ingrid; Legrand, Catherine
Source:
Biometrics 2019 v.75 no.1 pp. 297-307
ISSN:
0006-341X
Subject:
normal distribution,   etc   ; algorithms; biometry; models; variance;     Show all 5 Subjects
Abstract:
... Measurement error in the continuous covariates of a model generally yields bias in the estimators. It is a frequent problem in practice, and many correction procedures have been developed for different classes of models. However, in most cases, some information about the measurement error distribution is required. When neither validation nor auxiliary data (e.g., replicated measurements) are avail ...
DOI:
10.1111/biom.12960

2. Diagnosing misspecification of the random‐effects distribution in mixed models

Author:
Drikvandi, Reza; Verbeke, Geert; Molenberghs, Geert
Source:
Biometrics 2017 v.73 no.1 pp. 63-71
ISSN:
0006-341X
Subject:
normal distribution,   etc   ; algorithms; biometry; models;     Show all 4 Subjects
Abstract:
... It is traditionally assumed that the random effects in mixed models follow a multivariate normal distribution, making likelihood‐based inferences more feasible theoretically and computationally. However, this assumption does not necessarily hold in practice which may lead to biased and unreliable results. We introduce a novel diagnostic test based on the so‐called gradient function proposed by Ver ...
DOI:
10.1111/biom.12551

3. Bayesian additive adaptive basis tensor product models for modeling high dimensional surfaces: an application to high‐throughput toxicity testing

Author:
Wheeler, Matthew W.
Source:
Biometrics 2019 v.75 no.1 pp. 193-201
ISSN:
0006-341X
Subject:
normal distribution,   etc   ; Bayesian theory; United States Environmental Protection Agency; algorithms; biometry; data collection; dose response; models; toxicity testing;     Show all 9 Subjects
Abstract:
... Many modern datasets are sampled with error from complex high‐dimensional surfaces. Methods such as tensor product splines or Gaussian processes are effective and well suited for characterizing a surface in two or three dimensions, but they may suffer from difficulties when representing higher dimensional surfaces. Motivated by high throughput toxicity testing where observed dose‐response curves a ...
DOI:
10.1111/biom.12942

4. Estimation of sparse directed acyclic graphs for multivariate counts data

Author:
Han, Sung Won; Zhong, Hua
Source:
Biometrics 2016 v.72 no.3 pp. 791-803
ISSN:
0006-341X
Subject:
normal distribution,   etc   ; algorithms; animal ovaries; biometry; gene regulatory networks; high-throughput nucleotide sequencing; lognormal distribution; ovarian neoplasms; variance;     Show all 9 Subjects
Abstract:
... The next‐generation sequencing data, called high‐throughput sequencing data, are recorded as count data, which are generally far from normal distribution. Under the assumption that the count data follow the Poisson log‐normal distribution, this article provides an L1‐penalized likelihood framework and an efficient search algorithm to estimate the structure of sparse directed acyclic graphs (DAGs) ...
DOI:
10.1111/biom.12467
PubMed:
26849781
PubMed Central:
PMC4975686

5. Protein Bioinformatics and Mixtures of Bivariate von Mises Distributions for Angular Data

Author:
Mardia, Kanti V.; Taylor, Charles C.; Subramaniam, Ganesh K.
Source:
Biometrics 2007 v.63 no.2 pp. 505-512
ISSN:
0006-341X
Subject:
normal distribution,   etc   ; algorithms; bioinformatics; biometry; data collection; models; protein secondary structure;     Show all 7 Subjects
Abstract:
... A fundamental problem in bioinformatics is to characterize the secondary structure of a protein, which has traditionally been carried out by examining a scatterplot (Ramachandran plot) of the conformational angles. We examine two natural bivariate von Mises distributions--referred to as Sine and Cosine models--which have five parameters and, for concentrated data, tend to a bivariate normal distri ...
DOI:
10.1111/j.1541-0420.2006.00682.x
PubMed:
17688502