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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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 ...
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Productivity estimator

I wanted to estimate the productivity parameter in the production function. I estimated it using levinsohn and petrin (lp) method. It is a semi-parametric regression estimation. It takes raw material ...
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semiparametric index model with heteroskedasticity

I'm trying to estimate a semiparametric binary response model with index heteroscedasticity in R. That is, I have a model defined with $y_i = \mathbf{1}\{\beta_0 + \beta_1 x_{1i} + \beta_2 x_{2i} + \...
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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 ...
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how to parameter the gamess function?

i want to run gampackage to calculate threshold and semi parametric model, especialy partialy linear model like : Y = V1 + V2 + V3 + f(V4) where: Y is factor and dichotomic varible; V1 is continuous ...
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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 ...
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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, ...
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300 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{...
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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?
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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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How to make predictions from penalized spline model

Consider a piecewise linear function with $M$ knots: $Y_i = \beta_1 + \beta_2x_i + \beta_{21}(x_i-\kappa_1)_+ + \beta_{22}(x_i - \kappa_2)_+ + ... + \beta_{2M}(x_i-\kappa_M)_+ + e_i$ where $(x_i-\...
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Why are my PITs (probability of integral transforms) not uniform?

community! I have here hope not a silly R code where I try to use PITs (probability of integral transforms) to "diagnose" fit of a simulated distribution. Code starts here: ...
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Quadratic regressions with explanatory count variables

I am running an OLS model where my dependent variable Y is continous and among the explanatory vars I have a count variable X. I want to test if the effect of X on Y changes sing. To do so I would ...
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1answer
3k views

Gam with low E.D.F (estimated degrees of freedom) value in main effect, not interaction term

I have a gam model with the following structure: ...
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241 views

JuliaOpt Empirical Likelihood Estimation

I am trying to perform an empirical likelihood estimation in a regression setting using JuliaOpt (Convex or JuMP) and ran into difficulties using either API. The problem: Empirical likelihood for ...
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2answers
904 views

Understanding Big/Little $O_p$/$o_p$ Notation for Estimators

I am reading a Text about Single Index Models (SIM), where a SIM is defined as $E[Y|X=x] = G(X' \beta)$, with $G$ and $\beta$ unknown. After proposing an estimator for the function $G$, the ...
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Density Function Estimation

Given a sample of $n$ observations, which are assumed to be $i.i.d.$ and generated from a continuous probability law. Consider the question of estimating the density function $f(x)$. There are two ...
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Testing semi-parametric versus parametric model

I am estimating a (semi)parametric and a parametric model for a panel data set, and I want to test the functional form by applying the method proposed by Henderson et al. (2008, p.267). In particular, ...
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Generalized additive models — who does research on them besides Simon Wood?

I use GAMs more and more. When I go to provide references for their various components (smoothing parameter selection, various spline bases, p-values of smooth terms), they are all from one ...
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903 views

Book for introductory nonparametric econometrics/statistics

My work implies a lot of econometrics, and I had a good formation about it. Nevertheless, I am regularly faced with some semi or non parametric techniques (for instance I had to use quantile ...
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Implementation of semi parametric methods

Has anyone worked with semi parametric methods to estimate parameters with binary outcome? Examples are like Cosslett (1983) or Ichimura or Klein-Spady. In other words we are looking for semi ...
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727 views

Variance of plugin estimator

This question related to my previous question. Let $$X_1,\dots,X_n$$ are i.i.d. with distribution function $F$ and $$Y_1,\dots,Y_n$$ are i.i.d. with distribution function $G$. Suppose that there ...
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Do robust standard errors protect you from proportional odds assumptions?

Cox Proportional Hazards models are traditionally taught alongside proportional hazards assumptions. There is a corresponding test of proportionality. However, if standard errors are calculated from ...
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When is quantile regression worse than OLS?

Apart from some unique circumstances where we absolutely must understand the conditional mean relationship, what are the situations where a researcher should pick OLS over Quantile Regression? I don'...