Results 51 to 60 of about 27,081 (245)
Nonparametric and semiparametric methods for economic research [PDF]
, 1992 Developments in the vast and growing literatures on nonparametric and semiparametric statistical estimation are reviewed. The emphasis is on useful methodology rather than statistical properties for their own sake. Some empirical applications to economic
Robinson, Peter M., Delgado, Miguel A.
core
Learning sparse mixture‐of‐experts generalized linear models in ultrahigh dimensions
Canadian Journal of Statistics, EarlyView.Abstract Motivated by the challenges of heterogeneity and high dimensionality in the analysis of modern data, we investigate continuous regularization methods for learning sparse mixture‐of‐experts generalized linear models (MoE‐GLM). Although there are foundational results about regularized estimators in a broad class of regression models including ...
Pengqi Liu, Abbas Khalili
wiley +1 more source
Semi- and Nonparametric ARCH Processes
Journal of Probability and Statistics, 2011 ARCH/GARCH modelling has been successfully applied in empirical finance for many years. This paper surveys the semiparametric and nonparametric methods in univariate and multivariate ARCH/GARCH models.
Oliver B. Linton, Yang Yan
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Journal of Forecasting, EarlyView.ABSTRACT This paper presents a method for forecasting limit order book durations using a self‐exciting flexible residual point process. High‐frequency events in modern exchanges exhibit heavy‐tailed interarrival times, posing a significant challenge for accurate prediction.
Kyungsub Lee
wiley +1 more source
Semiparametric conformal prediction
CoRRMany risk-sensitive applications require well-calibrated prediction sets over multiple, potentially correlated target variables, for which the prediction algorithm may report correlated errors. In this work, we aim to construct the conformal prediction set accounting for the joint correlation structure of the vector-valued non-conformity scores ...
Ji Won Park +2 more
openaire +3 more sources
Model and Variable Selection Procedures for Semiparametric Time Series Regression
Journal of Probability and Statistics, 2009 Semiparametric regression models are very useful for time series analysis. They facilitate the detection of features resulting from external interventions.
Risa Kato, Takayuki Shiohama
doaj +1 more source
Analysis of The Debtor's Endurance using Cox Regression Semiparametric Method
Jurnal Ilmu Keuangan dan Perbankan, 2022 The aim of this research was conducted to determine the factors that influence the resilience of car loan debtors in an area. The research method used is semiparametric Cox regression on secondary data, WAREHOUSE consisting of the customer profile ...
Vitri Aprilla Handayani* +3 more
doaj +1 more source
Journal of Forecasting, EarlyView.ABSTRACT This paper develops Masked Asset–Regime Scenario Diffusion (MARS‐Diff), a leakage‐disciplined framework for probabilistic forecasting of multiday portfolio losses. The framework combines a regularized heterogeneous autoregressive model with exogenous predictors (HAR‐X) as its anchor, a train‐only masked representation of a high‐dimensional ...
Çağlar Sözen, Mervenur Sözen
wiley +1 more source
Semiparametric optimal estimation with nonignorable nonresponse data [PDF]
, 2021 When the response mechanism is believed to be not missing at random (NMAR), a valid analysis requires stronger assumptions on the response mechanism than standard statistical methods would otherwise require.
Kim, Jae Kwang, Morikawa, Kosuke
core
STATISTICAL MODELING OF TOURISM INVESTMENT DECISIONS IN INDONESIA USING SEMIPARAMETRIC APPROACH
BarekengThe tourism potential in Indonesia is very large considering that Indonesia consists of tens of thousands of separate islands. Indonesia has many diverse landscapes, with all its nature wealth and biodiversity in it is an attraction for investors who ...
Yossy Maynaldi Pratama +4 more
doaj +1 more source