Measurement and Inference
Models, measurement and inference in epithelial tissue dynamics
... for measurement and parameter estimation (challenge III), referenc- ing examples from tumour dynamics and epithelial development and repair, while emphasising the intertwined nature of the three challenges ...
21
Estimation and Inference of Threshold Regression Models with Measurement Errors
... Introduction Measurement error is a common problem in economic ...to measurement errors because of data aggregation or for other ...of measurement errors results in inconsistent estimation of ...
27
Probabilistic Matching: Causal Inference under Measurement Errors
... Causal inference when important variables are missing is mainly based on structural equation models (SEMs) with latent variables [7], ...the measurement model describing the relationships of the latent ...
8
Measurement-Based Network Monitoring and Inference: Scalability and Missing Information
... and Inference: Scalability and Missing Information Chuanyi Ji, Member, IEEE and Anwar Elwalid Abstract—Using measurements collected at network monitors to infer network conditions is a promising approach for net- ...
12
Balanced Scorecard with Fuzzy Inference as a Performance Measurement in an Automotive Manufacturing line
... performance measurement based on both financial and non- financial information from four perspectives which are called financial, customer, internal business process, and learning and growth, in order to balance ...
8
Identification and Inference of Nonlinear Models Using Two Samples with Arbitrary Measurement Errors
... related two samples. But, in most empirical applications, the latent models of interest are parametric nonlinear models and the two samples are regarded as independent. Within this framework, we propose a sieve quasi ...
59
Sensitivity Analysis for Causal Inference Under Unmeasured Confounding and Measurement Error Problems
... A commonly used method for identifying the otherwise unidentifiable parameter of interest is to assume parametric models that encode stringent assumptions about the data generating mechanism. This approach has been ...
14
A Rao-Blackwellized particle filter with variational inference for state estimation with measurement model uncertainties
... variational inference for jointly estimating state and time- varying parameters in non-linear state-space models with non-Gaussian measurement ...Bayesian inference in an auxiliary particle filter ...
12
Measurement and Inference in International Reserve Diversification
... This section applies the moving average exchange rate conversion methodology discussed in previous sections to calculate the reserve currency quantity shares estimated from three sets o[r] ...
63
Seeing and Believing: Detection, Measurement, and Inference in Experimental Physics
... modern experimental physics that we rarely call them out for special notice. For example, one important question was to make sure that the particles he detected were really coming from[r] ...
11
Measurement, Consequences, and Debiasing of Correspondent Inference Making
... correspondent inference-making are able to make more accurate judgments when situational information is provided, individuals more prone to correspondent inference-making are more resistant to the use of ...
7
Measurement error and causal inference with instrumental variables
... Causal Inference 1 Introduction Causal inference deals with cause-effect relationships between interventions (ex- posures) and outcomes (responses) in many fields of study including biostatistics, ...
195
The predictive accuracy of credit ratings: measurement and statistical inference
... 6. Conclusions In analyzing measures for the predictive accuracy of rating systems, this paper contributes to the existing literature mainly in two aspects. First, we propose a measure from the biostatistical literature, ...
20
The predictive accuracy of credit ratings: Measurement and statistical inference
... 6. Conclusions In analyzing measures for the predictive accuracy of rating systems, this paper contributes to the existing literature mainly in two aspects. First, we propose a measure from the biostatistical literature, ...
20
High dimensional inference: structured sparse models and non-linear measurement channels
... volving linear measurements and General Linear Models (GLMs). We begin this section with a formal definition of RE condition:.. RN is a condition on sensing matrices a[r] ...
151
Automatic inference and measurement of 3D carpal bone kinematics from single view fluoroscopic sequences
... In further work, we will extend the current statistical model with more training data (in progress), and improve the measurement model by including more healthy subjects. A larger training set may allow us a ...
12
A New Structure for Direct Measurement of Temperature Based on Negative Temperature Coefficient Thermistor and Adaptive Neuro-fuzzy Inference System
... Figure 7. Error rates resulting from three methods when applied on the test data 5. CONCLUSIONS Issues such as self-heating phenomenon and the simplifying assumptions degrade the functional accuracy of systems which ...
7
Inference of surface concentrations of nitrogen dioxide (NO2) in Colombia from tropospheric columns of the ozone measurement instrument (OMI)
... Keywords: Inference of nitrogen dioxide surface concentration, density of tropospheric columns, OMI, GEOS-Chem, fire radiative power, chemiluminescence interference, overestimation, ...
22
Inference on Survival Data with Covariate Measurement Error - An Imputation-based Approach
... In this article, we have extended the Cox partial likelihood approach to t survival models with covariate measurement errors. Our key idea is to impute the unobserved covariates based on their conditional ...
35
Variational Inference for Logical Inference
... Abstract Functional Distributional Semantics is a framework that aims to learn, from text, semantic representations which can be in- terpreted in terms of truth. Here we make two contributions to this framework. The ...
10