scholarly journals An ANOVA test for parameter estimability using data cloning with application to statistical inference for dynamic systems

2014 ◽  
Vol 70 ◽  
pp. 257-267 ◽  
Author(s):  
David Campbell ◽  
Subhash Lele
Keyword(s):  
Statistical Inference ◽  
Dynamic Systems ◽  
Anova Test ◽  
Data Cloning ◽  
Using Data

Encountering data

2019 ◽  
pp. 5-10
Author(s):  
M. D. Edge
Keyword(s):  
Probability Theory ◽  
Statistical Inference ◽  
Statistical Estimation ◽  
The World ◽  
Using Data

Statistics is concerned with using data to learn about the world. In this book, concepts for reasoning from data are developed using a combination of math and simulation. Using a running example, we will consider probability theory, statistical estimation, and statistical inference. Estimation and inference will be considered from three different perspectives.

scholarly journals Análisis del grado de sensibilización del empresario ante el problema del empleo de trabajadores con discapacidad en la provincia de Cádiz

2009 ◽  
pp. 205
Author(s):  
Héctor M. Ramos Romero ◽  
Antonio Leal Jiménez
Keyword(s):  
Company Size ◽  
Business Activity ◽  
Indicator Values ◽  
Disabled People ◽  
Anova Test ◽  
Using Data ◽  
Disabled Workers

Proponemos en este trabajo un indicador para evaluar el grado de sensibilización del empresario ante el problema de la contratación de trabajadores con discapacidad en la provincia de Cádiz. A partir de los datos obtenidos a través de entrevistas personales a empresarios, llevamos a cabo un análisis de la varianza multifactorial y estudiamos la influencia sobre el indicador de factores como son el sector de actividad de la empresa, la existencia previa de trabajadores con discapacidad y el tamaño de la empresa, así como sus interacciones.<br /><br />This paper presents an indicator to evaluate employer's attitudes towards the employment of disabled people in the Province of Cadiz. Using data collected during personal interviews with employers, we applied a multifactor ANOVA test to analyse which factors influence the indicator values. The factors considered are the sector of business activity, the company size, the prior existence of disabled workers in the company, and their interactions.<br />

scholarly journals Statistical analysis and optimality of neural systems

2019 ◽  
Author(s):  
Wiktor Młynarski ◽  
Michal Hledík ◽  
Thomas R. Sokolowski ◽  
Gašper Tkačik
Keyword(s):  
Statistical Inference ◽  
Biological Function ◽  
A Priori ◽  
Normative Theory ◽  
Neural Systems ◽  
Model Parameters ◽  
C Elegans ◽  
Using Data ◽  
Ab Inito ◽  
Regime Characteristic

Normative theories and statistical inference provide complementary approaches for the study of biological systems. A normative theory postulates that organisms have adapted to efficiently solve essential tasks, and proceeds to mathematically work out testable consequences of such optimality; parameters that maximize the hypothesized organismal function can be derived ab initio, without reference to experimental data. In contrast, statistical inference focuses on efficient utilization of data to learn model parameters, without reference to any a priori notion of biological function, utility, or fitness. Traditionally, these two approaches were developed independently and applied separately. Here we unify them in a coherent Bayesian framework that embeds a normative theory into a family of maximum-entropy “optimization priors.” This family defines a smooth interpolation between a data-rich inference regime (characteristic of “bottom-up” statistical models), and a data-limited ab inito prediction regime (characteristic of “top-down” normative theory). We demonstrate the applicability of our framework using data from the visual cortex, the retina, and C. elegans, and argue that the flexibility it affords is essential to address a number of fundamental challenges relating to inference and prediction in complex, high-dimensional biological problems.

Model checking Petri nets with names using data-centric dynamic systems

2016 ◽  
Vol 28 (4) ◽  
pp. 615-641 ◽  
Author(s):  
Marco Montali ◽  
Andrey Rivkin
Keyword(s):  
Model Checking ◽  
Petri Nets ◽  
Dynamic Systems ◽  
Using Data

scholarly journals Assessing Parameter Identifiability in Phylogenetic Models Using Data Cloning

2012 ◽  
Vol 61 (6) ◽  
pp. 955-972 ◽  
Author(s):  
José Miguel Ponciano ◽  
J. Gordon Burleigh ◽  
Edward L. Braun ◽  
Mark L. Taper
Keyword(s):  
Parameter Identifiability ◽  
Data Cloning ◽  
Using Data ◽  
Phylogenetic Models

Hierarchical models in ecology: confidence intervals, hypothesis testing, and model selection using data cloning

Ecology ◽  
2009 ◽  
Vol 90 (2) ◽  
pp. 356-362 ◽  
Author(s):  
José Miguel Ponciano ◽  
Mark L. Taper ◽  
Brian Dennis ◽  
Subhash R. Lele
Keyword(s):  
Model Selection ◽  
Hypothesis Testing ◽  
Confidence Intervals ◽  
Hierarchical Models ◽  
Data Cloning ◽  
Using Data

scholarly journals Dealing with multivariate missing data in principal component analyses and subsequent model estimation: a two-step worked example using data from the Canadian Longitudinal Study of Aging

2019 ◽  
Author(s):  
Anni Hämäläinen ◽  
Paul Mick
Keyword(s):  
Longitudinal Study ◽  
Missing Data ◽  
Multiple Imputation ◽  
Statistical Inference ◽  
Principal Component ◽  
Diagnostic Tools ◽  
Parameter Estimates ◽  
Worst Case ◽  
Using Data ◽  
Imputation Approach

Missing data can be a significant problem for statistical inference in many disciplines when information is not missing completely at random. In the worst case, it can lead to biased results when participants or subjects with certain characteristics contribute more data than other participants. Multiple imputation methods can be used to alleviate the loss of sample size and correct for this potential bias. Multiple imputation entails filling in the missing data using information from the same and other participants on the variables of interest and potentially other available data that correlate with the variables of interest. The missing data estimates and uncertainty associated with their estimation may then be taken into account in statistical inference from those variables. A complication may arise when using compound variables, such as principal component loadings (PC), which draw on a number of raw variables that themselves have non-overlapping missing data. Here, we propose a sequential multiple imputation approach to facilitate the use of all available data in the raw variables contained in compound variables in a way that conforms to the specifications of the multiple imputation framework. We first use multiple imputation to impute missing data for the subset of raw variables used in a principal component analysis (PCA) and perform the PCA with the imputed data; then, use the factor loadings to calculate PC scores for each individual with complete raw data. Finally, we include these PC scores as part of a global multiple imputation approach to estimate a final statistical model. We demonstrate (including annotated Stata code) the use of this approach by examining which sensory, health, social and cognitive factors explain self-reported sensory difficulties in the Canadian Longitudinal Study of Aging (CLSA) Comprehensive Cohort. The proposed sequential multiple imputation approach allows us to deal with the issue of having large cumulative amount of data that is missing (not completely at random) among a large number of variables, including composite cognitive scores derived from a battery of cognitive tests. We examine the resulting parameter estimates using a range of recommended diagnostic tools to highlight the potential and consequences of the approach to the statistical results.

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