Search Results - subject:"missing data"

Likelihood based inference for diffusion driven models

Shephard, Neil; Chib, Siddhartha - Department of Economics, Oxford University - 2004

This paper provides methods for carrying out likelihood based inference for diffusion driven models, for example discretely observed multivariate diffusions, continuous time stochastic volatility models and counting process models. The diffusions can potentially be non-stationary. Although our...

Persistent link: https://www.econbiz.de/10010661411

Modelling Specific Interest Rate Risk with Estimation of Missing Data

Siegl, Thomas; Quell, Peter - In: Applied Mathematical Finance 12 (2004) 3, pp. 283-309

quantifies the amount of risk associated with missing data. The credit risk component is based on event risk caused by correlated …

Persistent link: https://www.econbiz.de/10009279061

Promotion Carryover as a Missing-Data Problem

Holloway, Garth J.; Aydogus, Osman - In: Journal of Agricultural & Food Industrial Organization 2 (2004) 1

An important feature of agribusiness promotion programs is their lagged impact on consumption. Efficient investment in advertising requires reliable estimates of these lagged responses and it is desirable from both applied and theoretical standpoints to have a flexible method for estimating...

Persistent link: https://www.econbiz.de/10014601249

Estimation of regression models with equi-correlated responses when some observations on the response variable are missing

Toutenburg, H.; Shalabh - In: Statistical Papers 44 (2003) 2, pp. 217-232

Persistent link: https://www.econbiz.de/10004970886

Imputation by power transformation

Singh, Sarjinder; Deo, Balbinder - In: Statistical Papers 44 (2003) 4, pp. 555-579

Persistent link: https://www.econbiz.de/10008533847

An Approximate Test for Homogeneity of Correlated Correlation Coefficients

Raghunathan, Trivellore - In: Quality & Quantity: International Journal of Methodology 37 (2003) 1, pp. 99-110

Persistent link: https://www.econbiz.de/10009391167

Methods for the Analysis of Explanatory Linear Regression Models with Missing Data Not at Random

Pastor, José Navarro - In: Quality & Quantity: International Journal of Methodology 37 (2003) 4, pp. 363-376

Persistent link: https://www.econbiz.de/10009391326

Modeling Blank Data Entries in Data Envelopment Analysis

Kuosmanen, Timo - EconWPA - 2002

We show how Data Envelopment Analysis (DEA) can handle missing data. When blank data entries are coded by appropriate … dummy values, the DEA model automatically excludes the missing data from the analysis. We extend this result to weight … restriction in case of missing data. Our approach is illustrated by a case study, describing an application to international …

Persistent link: https://www.econbiz.de/10005556325

Nonparametric estimation of distribution and density functions in presence of missing data: an IFS approach

Iacus, Stefano; Torre, Davide La - Dipartimento di Economia, Management e Metodi … - 2002

density estimation) in presence of missing data, i.e. when it is only possibile to observe data on subsets of the whole …

Persistent link: https://www.econbiz.de/10005007308

Application of the European Customer Satisfaction Index to Postal Services. Structural Equation Models versus Partial Least Squares

O'Loughlin, Christina; Coenders, Germà - Departament d'Economia, Facultat de Ciències … - 2002

more recent advances in SEM, including estimation methods that are robust to non-normality and missing data. In this paper …

Persistent link: https://www.econbiz.de/10005051018