Results 111 to 120 of about 501,333 (190)

Personalized treatment selection using observational data. [PDF]

J Appl Stat, 2023
Kulasekera KB, Tholkage S, Kong M.
europepmc   +1 more source

Re-weighted estimation of the transition probability density for second-order diffusion processes. [PDF]

PLoS One
Li Y, Wang Y, Tang M.
europepmc   +1 more source

A note on unconditional properties of a parametrically guided Nadaraya-Watson estimator

Asymptotic properties of a semiparametric regression estimator proposed in Glad (1996) are derived, without conditioning on the predictor variables. The leading terms of unconditional asymptotic bias and variance are equal to those in the expressions ...
Glad, Ingrid K.
core  

MonotonicityTest: An R Package for Efficient Nonparametric Monotonicity Testing. [PDF]

Obs Stud
Huynh D, Parast L.
europepmc   +1 more source

Local linear smoothing for regression surfaces on the simplex using Dirichlet kernels. [PDF]

Stat Pap (Berl)
Genest C, Ouimet F.
europepmc   +1 more source

Uniform in Bandwidth Consistency of the <i>L</i>¹-Modal Regression Estimator for High-Dimensional Data. [PDF]

Entropy (Basel)
Almulhim FA, Alamari MB, Laksaci A.
europepmc   +1 more source

On the Reweigted Nadaraya-Watson Estimator of the Conditional Density Function

, 2015
The conditional probability density function plays an important role in statistics. It describes the relationship between two random variables, the dependent variable Y and the independent variable X. In this thesis, we studied the kernel estimation of the conditional density function when it is unknown.
openaire  

Inverse-Probability-Weighted Kernel Estimation of Regression Derivatives Under Missing-at-Random Responses for Stationary Ergodic Processes. [PDF]

Entropy (Basel)
Bouzebda S, Didi S.
europepmc   +1 more source

A new time-varying coefficient regression approach for analyzing infectious disease data. [PDF]

Sci Rep, 2023
Liu J, Bellows B, Hu XJ, Wu J, Zhou Z, Soteros C, Wang L.   +6 more
europepmc   +1 more source

Efficient non-parametric estimation of variable productivity Hawkes processes. [PDF]

J Appl Stat
Phillips S, Schoenberg F.
europepmc   +1 more source