The data, variables and methods
Quantifying Vegetation Biophysical Variables from Imaging Spectroscopy Data: A Review on Retrieval Methods
... state variables, the approach is generic and generally ...many variables and consequently challenging to invert, whereas simpler models may be less realistic but easier to ...the variables to be ...
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Methods for Clustered Competing Risks Data and Causal Inference using Instrumental Variables for Censored Time-to-event Data
... aforementioned methods and a few others ...these methods are not suited for causal inference from observational ...the methods that do permit inclusion of covariates, Mark and Robins (1993) ...
126
Selection of Variables that Influence Drug Injection in Prison: Comparison of Methods with Multiple Imputed Data Sets
... S2 methods suggest that, in a screening round, variables reached significance level in at least 10% or 50% of generated data sets, and can be selected as candidate for multifactorial ...selects ...
9
Practical Methods Validation For Variables Selection In The High Dimension Data: Application For Three Metabolomics Datasets
... relevant variables for sample classification is a common task in most features expression studies, including this ...by data quality: redundant, noisy, and unreliable information as well as a confusing ...
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Combining information on multiple instrumental variables in Mendelian randomization: comparison of allele score and summarized data methods.
... instrumental variables to obtain causal inferences from observa- tional ...instrumental variables (IVs) into a single causal estimate are as follows: (i) allele scores, in which individual-level data ...
28
Methods for Dealing with Death and Missing Data, and for Standardizing Different Health Variables in Longitudinal Datasets: The Cardiovascular Health Study
... Methods for dealing with death and missing data, and for standardizing different health variables in longitudinal datasets: the Cardiovascular Health Study Introduction: Death, missingness, and ...
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Estimation methods in the errors-in-variables context
... these methods, the proposed method guarantees that the speci- fied curve class is fitted, whereas Taubin’s method and hyper-accurate ellipse fitting, despite what their name might suggest, may be misled by noise ...
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ALARM DETECTION METHODS FOR PHYSIOLOGICAL VARIABLES
... 1 Introduction The development of automatic control systems for the conti- nuous administration of drugs has been a subject of interest in the last decades and, in particular, for the control of the neu- romuscular ...
5
Imputation Methods for Incomplete Dependent Variables in Finance
... explanatory variables (i.e., including too few variables in the missingness ...Missing data problems should be taken seriously in many financial ...explicit data information with regard to ...
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Lasso Methods for Gaussian Instrumental Variables Models
... instrumental variables (IV) models with many instruments in the canonical Gaussian ...The methods apply even when the number of instruments is much larger than the sample ...a data-driven penalty ...
35
Nonparametric methods for inference in the presence of instrumental variables
... Closely related inverse problems, where the context is rendered relatively abstract in order to facilitate solution, include those studied by Donoho [ 4 ], Johnstone [ 8 ] and Cavalier, Golubev, Picard and Tsybakov [ 2 ...
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Parametric methods outperformed non-parametric methods in comparisons of discrete numerical variables
... distributed variables, the two- sample T test and confidence interval are well known to be the optimal methods for comparing the ...nonnormal data, and especially skewed data, that alternative ...
8
Dimensionality reduction methods for contingency tables with ordinal variables
... cross-classified data: Reconcil- iation and synthesis of some methods of Pearson, Yule, and Fisher, and also some methods of correspondence analysis and association ...
14
The Effectiveness of Forecasting Methods Using Multiple Information Variables
... subsequent data, but we suspect that this recursive estimation is more subject to the effects of changes in economic structure and in the information contained by variables, as the sample length ...
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Large Margin Methods for Structured and Interdependent Output Variables
... We have performed experiments using a document collection released by the World Intellectual Property Organization (WIPO), which uses the International Patent Classification (IPC) scheme. We have restricted ourselves to ...
32
Methods for handling missing variables in risk prediction models
... predictor variables in otherwise suitable validation cohorts are the main factor precluding external ...patient data from 9 different cohort studies conducted in the United States, Europe, and Latin America ...
8
Methods of Economic Theory: Variables, Transactions and Expectations as Functions of Risks
... 2010; Moody ’ s, 2010; S&P&, 2016; Fitch, 2018) risk ratings take values of risk grades like AAA, AA, BB, C etc. Different rating agencies use different risk assessment methodologies and risk grades notions differs ...
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Variable Importance and Prediction Methods for Longitudinal Problems with Missing Variables
... binary variables in terms of their importance for developing a medical out- come, which is a very common problem in variable importance ...art methods for causal inference to solve prediction and VIM ...
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Sensibility to noise of new multifractal fusion methods for ocean variables
... shown to leave a recognisable footprint in many different variables. This common footprint is the result of similar non- linear terms within the equations defining the evolution of ocean scalars (such as sea ...
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Comparison of methods for imputing limited-range variables: a simulation study
... the data are either missing com- pletely at random (MCAR), that is the missingness does not depend on observed or unobserved data, or are missing at random (MAR), where the probability of a value being ...
11