# Questions tagged [semiparametric]

Semiparametric probability models are a general class of models used for estimation and inference that contain a nonparametric component and a parametric component.

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### Semiparametric theory worked examples

Does anyone know any resources for worked problems in Semiparametric theory? I'm currently reading Tsiatis 2006 and am looking for examples. Thanks!
57 views

### estimation method in GAM model

I created a GAM model with semiparametric with parametric and nonparametric covariates. In the parametric regression model there is an estimation method to determine the value of the beta coefficient. ...
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### Choosing variables for a semiparametric model

I am trying to create a semiparametric model for university (we were told it HAS to be semiparametric) and I have 11 response variables, some of them categorical and the rest continuous. In the simple ...
64 views

### MCMC fitting of a Dirichlet Process or Polya Tree prior to the residuals in a (simple linear regression)/(2-independent-samples) problem

Consider a simple location-shift semi-parametric model with two mutually-independent samples (here $F$ is a cumulative distribution function (CDF) on $\mathbb{ R }$, the $C_i$ and $T_j$ are real-...
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### Panel regression with an added smooth function

I would like to estimate the following regression model $$\log p_{it} = x_{it}^\top\beta + \mu_{m(i,t),t} + \epsilon_{it}$$ where the dependent variable is log housing prices and $x_{it}$ are housing ...
23 views

### Local Data Generating Process in Semiparametric Statistics

I am a bit confused about the LDGP assumption that is mentioned in books on semiparametric statistics. For example, in Semiparametric Theory and Missing Data by Tsiatis, the LDGP is defined as follows:...
75 views

### Efficient influence function in proportional hazards model

I was hoping someone could help me with this problem in the cox proportional hazards model. I am given the following setup. T is a non-negative random variable with continous distribution and hazard ...
33 views

### Martingale in Cox Model

Can someone help me to show that $$\hat{A}(t, \beta_0) = \sum_{i=1}^{n} \int_0^t\frac{1}{\sum_{j}^n Y_j(s) e^{X_i^T \beta_0}} dN_i(s)$$ is a martingale. The setup is the Cox proportional hazard ...
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### INLA in R for fitting parametric and semiparametric (Cox-like) survival models with frailties

I am exploring different methods of fitting survival models in R. One method of interest utilizes INLA for fitting Cox-type semi-parametric survival models. I would like to compare fits from INLA to ...
19 views

### Local regressions with many zeroes

I want to run a nonparametric or semiparametric regression on data which I suspect to be non-linear. I'm using Stata for this. At first I thought of using LOWESS ...
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### Convergence of a semiparametric estimator - a doubt

Suppose we have a parametric continuous function of $x\in\mathbb{R}$ with d-dimensional parameter $\theta$ $$g(x;\theta)$$ we also have have an n-dimensional sample if i.i.d. observations of X. With ...
91 views

### OLR with rms: proportional odds assumption

I am fitting an ordinal logistic regression model with rms package. my data involves a three-level ordered outcome (see ...
81 views

### How Semiparametric regression works?

I am working on semiparametric regression models; $$y=\beta x_1 +m(x_2)+e$$. I can understand this combination of Parametric and Nonparametric but how to estimate the responses ($\hat y$)? What is ...
561 views

### Is the Wilcoxon two-sample test maximally powered to detect proportional odds alternatives?

We know from the literature that The Wilcoxon-Mann-Whitney two-sample rank sum test is optimal for detecting simple location shifts when comparing two continuous random variables that each have a ...
456 views

### Understanding Sharpe Ratio Hypothesis Testing - Ledoit + Wolf

I've been poring over this paper written by Ledoit and Wolf regarding their approach to constructing hypothesis tests for Sharpe Ratios. In short, they see that running circular block bootstrap ...
3k views

### In survival analysis, when should we use fully parametric models over semi-parametric ones?

This question is the counterpoint of the other question In survival analysis, why do we use semi-parametric models (Cox proportional hazards) instead of fully parametric models? Indeed, it clearly ...
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### What is the degree of freedom of semiparametric method for mixture distribution

In the semi-parametric method for density analysis, I want to compare one component semi-parametric mixture distribution and two components mixture distribution. Semi-parametric here means the shape ...
19 views

### Deconvoluting an ECDF via mixed modeling

I have data with measurement error, $W_i$, with the following structure: $$W_i = \mu + \gamma_i + U_i$$ where $U_i \sim N(0, \sigma^2_i)$, with known $\sigma^2_i$, and $U_i \; \amalg \; \gamma_i$. I ...
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### Nonparametric regression on data with known noise parameterization

What's the best way to regress on data for which we don't have a parameterised generative model (e.g. an arbitrary non-smooth continuous signal, that can be regressed in model-free ways with splines, ...
591 views

### Probabilistic interpretation of Thin Plate Smoothing Splines

TLDR: Do thin plate regression splines have a probabilistic/Bayesian interpretation? Given input-output pairs $(x_i,y_i)$, $i=1,...,n$; I want to estimate a function $f(\cdot)$ as follows \begin{...
575 views

### Is quantile regression model a parametric approach?

Is quantile regression a parametric regression or it is semiparametric? If it is parametric then what are its assumptions and if semiparametric then how?
593 views

### How general is the backfitting algorithm?

Hastie \& Tibshirani's original approach to fitting generalized additive models was the backfitting algorithm. For a model of the form $$y = \alpha + \displaystyle\sum_k f_k(x_k) + \epsilon$$ ...
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