Latent Variable Regression Analysis with Missing Covariates: LikelihoodBased Methods and Applications,Used

Latent Variable Regression Analysis with Missing Covariates: LikelihoodBased Methods and Applications,Used

In Stock
SKU: DADAX383832157X
UPC: 9783838321578
Brand: LAP Lambert Academic Publishing
Condition: New
Regular price$130.43
Free Standard Shipping Across USA
Quantity
Add to wishlist
Add to compare

Sold by Ergodebooks, an authorized reseller.

Returns accepted within 30 days | support@ergodebooks.com

Verified
Shipping Information
  • Free Standard Shipping — United States only
  • Processing Time: 3–5 business days
  • Estimated Delivery: 6–10 business days after dispatch
  • Double-boxed, fully insured & discreetly packaged
  • Tracking number sent via email once dispatched
Returns & Refund

Returns accepted within 30 days of delivery.

Damaged or Defective Item

Free return shipping + replacement or full refund

Wrong Item Received

Free return shipping + replacement or full refund

Change of Mind

Return shipping at customer's expense · 25% restocking fee applies

All returns require a Return Authorization (RA) number before sending.

To initiate a return, contact us:

support@ergodebooks.com +1 (281) 738-1050
View Full Return & Refund Policy
Payment Option
Payment Methods

Help

If you have any questions, you are always welcome to contact us. We'll get back to you as soon as possible, withing 24 hours on weekdays.

Customer service

All questions about your order, return and delivery must be sent to our customer service team by e-mail at yourstore@yourdomain.com

Sale & Press

If you are interested in selling our products, need more information about our brand or wish to make a collaboration, please contact us at press@yourdomain.com

Missing data often arises in regression analysis either by study design or stochastic censoring. Restriction of analysis to complete observations may yield biased inferences. Developing likelihoodbased methods for analyzing missing data in a regression setting has largely focused on missing values in the dependent variable. In this book, we discuss two likelihoodbased approaches to inference for the regression of multivariate categorical outcomes on a set of covariates when some of the covariate values are missing. Specifically, this research seeks to develop methodologies in the context of latent variable models that (i) synthesize multiple outcomes into an latent construct that is easily interpretable yet retains relevant heterogeneity in individual outcomes; (ii) account for measurement inaccuracy in observable outcomes; (iii) model the association between the latent construct and covariates; (iv) handle missing covariate data in both ignorable and nonignorable cases. This book should be of particular interest to psychosocial scientists and others who plan to use latent variables models, but are discouraged by the daunting analytical difficulties associated with missing data.

⚠️ WARNING (California Proposition 65):

This product may contain chemicals known to the State of California to cause cancer, birth defects, or other reproductive harm.

For more information, please visit www.P65Warnings.ca.gov.

Recently Viewed