The following pages link to Majid Mojirsheibani (Q221746):
Displaying 46 items.
- An asymptotically optimal kernel combined classifier (Q334008) (← links)
- On the correct regression function (in \(L_{2}\)) and its applications when the dimension of the covariate vector is random (Q447623) (← links)
- A weighted bootstrap approximation of the maximal deviation of kernel density estimates over general compact sets (Q450878) (← links)
- Some results on classifier selection with missing covariates (Q453718) (← links)
- Kernel regression estimation for incomplete data with applications (Q513697) (← links)
- On the \(L_p\) norms of kernel regression estimators for incomplete data with applications to classification (Q518885) (← links)
- Classification when the covariate vectors have unequal dimensions (Q629136) (← links)
- On a weighted bootstrap approximation of the \(L_p\) norms of kernel density estimators (Q894575) (← links)
- A simple method for combining estimates to improve the overall error rates in classification (Q906142) (← links)
- Nonparametric estimation of level sets under minimal assumptions (Q956388) (← links)
- A note on the strong approximation of the smoothed empirical process of \(\alpha\)-mixing sequences (Q995838) (← links)
- (Q1375864) (redirect page) (← links)
- A consistent combined classification rule (Q1375865) (← links)
- A kernel-based combined classification rule (Q1573273) (← links)
- An almost surely optimal combined classification rule (Q1604616) (← links)
- The Glivenko-Cantelli theorem based on data with randomly imputed missing values (Q1612946) (← links)
- Classification with incomplete functional covariates (Q1642427) (← links)
- Empirical measures for incomplete data with applications (Q1952014) (← links)
- Kernel classification with missing data and the choice of smoothing parameters (Q2010808) (← links)
- On classification with nonignorable missing data (Q2034467) (← links)
- On histogram-based regression and classification with incomplete data (Q2044763) (← links)
- On the maximal deviation of kernel regression estimators with NMAR response variables (Q2093143) (← links)
- A simple approach to construct confidence bands for a regression function with incomplete data (Q2176328) (← links)
- On the performance of weighted bootstrapped kernel deconvolution density estimators (Q2208395) (← links)
- A note on the performance of bootstrap kernel density estimation with small re-sample sizes (Q2244602) (← links)
- On nonparametric classification with missing covariates (Q2373449) (← links)
- Nonparametric curve estimation with missing data: a general empirical process approach (Q2643271) (← links)
- On classification with incomplete covariates (Q3106399) (← links)
- A nearest-neighbor-based ensemble classifier and its large-sample optimality (Q3389615) (← links)
- A Note on Nonparametric Regression with β-Mixing Sequences (Q3585316) (← links)
- Some results on bootstrap prediction intervals (Q4344828) (← links)
- (Q4391130) (← links)
- A COMPARISON STUDY OF SOME COMBINED CLASSIFIERS (Q4416915) (← links)
- A generalized multinomial discriminant procedure with applications (Q4455493) (← links)
- Combining Classifiers via Discretization (Q4541235) (← links)
- On density and regression estimation with incomplete data (Q4605247) (← links)
- On statistical classification with incomplete covariates via filtering (Q5065290) (← links)
- Statistical Classification with Missing Covariates (Q5088199) (← links)
- Semi-doubly optimal concentric circles fitting with presence of heteroscedasticity (Q5107385) (← links)
- Aggregating classifiers via Rademacher–Walsh polynomials (Q5220783) (← links)
- Weighted bootstrapped kernel density estimators in two-sample problems (Q5266554) (← links)
- Some approximations to<i>L</i><sub><i>p</i></sub>-statistics of kernel density estimators (Q5758159) (← links)
- Classifier selection from a totally bounded class of functions (Q5951992) (← links)
- An iterated classification rule based on auxiliary pseudo-predictors. (Q5958221) (← links)
- On regression and classification with possibly missing response variables in the data (Q6594919) (← links)
- A kernel-type regression estimator for NMAR response variables with applications to classification (Q6606047) (← links)
