• [2006MNRAS.369.1334S]: semiparametric technique based on a maximum likelihood (ML) approach and Voronoi tessellation (VT). Besides, I wonder if Section 3.3, the cluster detection algorithm works similarly to a source detection algorithm in high energy astrophysics if tight photon clusters indicate sources. By the way, what is the definition of sources? Depending on the definitions, determining the right thresholds for detections would change; however, it seems like (brute) Monte Carlo simulations i.e. empirical approaches are employed for setting thresholds. Please, note that my questionnaire is irrelevant to this paper, which I enjoyed reading very much.
• [2004MNRAS.347.1241S]: similar to the above because of the same methodology, ML, VT, and color slide/filter for cluster detection
• [2002AJ....123.1807G]: cut and enhance (CE) cluster detection method. From the abstract: The method is semiparametric, since it uses minimal assumptions about cluster properties in order to minimize possible biases. No assumptions are made about the shape of clusters, their radial profile, or their luminosity function. On the contrary, I wish they used nonparametric which seems more proper in a statistical sense instead of semiparametric judging from their methodology description.
• [2002A%26A...383.1100N]: statistics related keywords: time series; discrete Fourier transform; long range dependence; log-periodogram regression; ordinary least squares; generalized least squares. The semiparametric method section seems too short. Detail accounts are replaced by reference papers from Annals of Statistics. Among 31 references, 15 were from statistics journals and without reading them, average readers will not have a chance to understand the semiparametric approach.

You might want to check out wiki:Semiparametric about semiparametric (model) from the statistics standpoint.

The following books that I checked from libraries some years back related to semiparametric methods, from which you could get more information about semeparametric statistics. Unfortunately, applications and examples in these books are heavily rely on subjects such as public health (epidemiology), bioinformatics, and econometrics.

• Rupert, Wand, and Carroll (2003) Semiparametric Regression, Cambridge University Press
• Härdle, Müller, Sperlich, and Werwatz (2004) Nonparametric and Semiparametric Models, Spinger
• Horowitz (1998) Semiparametric Methods in Econometrics (Lecture Notes in Statistics) , Springer

There seem more recent publications from 2007 and 2008 about semiparametric methods, targeting diverse but focused readers but no opportunities for me to have a look on them. I just want to point out that many occasions we confront that full parametrization of a model is not necessary but those nuisance parameters determines the shape of a sampling distribution for accurate statistical inference. Semiparametric methods described in above papers are very limited from statistics viewpoints. Astronomers can take a way more advantages from various semiparametrical strategies. There are plenty of rooms for developing semiparametric approaches to various astronomical data analysis and inference about the parameters of interest. It is almost unexplored.

In addition to Bayesian methodologies, like this week’s astro-ph, studies on characterizing empirical spatial distributions of voids and galaxies frequently appear, which I believe can be enriched further with the ideas from stochastic geometry and spatial statistics. Click for what was appeared in arXiv this week.

• [astro-ph:0805.0156]R. D’Abrusco, G. Longo, N. A. Walton
  Quasar candidates selection in the Virtual Observatory era

• [astro-ph:0805.0201] S. Vegetti& L.V.E. Koopmans
  Bayesian Strong Gravitational-Lens Modelling on Adaptive Grids: Objective Detection of Mass Substructure in Galaxies (many like to see this paper: nest sampling implemented, discusses penalty function and tessllation)

• [astro-ph:0805.0238] J. A. Carter et al.
  Analytic Approximations for Transit Light Curve Observables, Uncertainties, and Covariances

• [astro-ph:0805.0269] S.M.Leach et al.
  Component separation methods for the Planck mission

• [astro-ph:0805.0276] M. Grossi et al.
  The mass density field in simulated non-Gaussian scenarios

• [astro-ph:0805.0790] Ceccarelli, Padilla, & Lambas
  Large-scale modulation of star formation in void walls
  [astro-ph:0805.0797] Ceccarelli et al.
  Voids in the 2dFGRS and LCDM simulations: spatial and dynamical properties

• [astro-ph:0805.0875] S. Basilakos and L. Perivolaropoulos
  Testing GRBs as Standard Candles

• [astro-ph:0805.0968] A. A. Stanislavsky et al.
  Statistical Modeling of Solar Flare Activity from Empirical Time Series of Soft X-ray Solar Emission

• [astro-ph:0803.0997] V. Smolcic et.al.
     A new method to separate star forming from AGN galaxies at intermediate redshift: The submillijansky radio population in the VLA-COSMOS survey
• [astro-ph:0803.1048] T.A. Carroll and M. Kopf
     Zeeman-Tomography of the Solar Photosphere — 3-Dimensional Surface Structures Retrieved from Hinode Observations
• [astro-ph:0803.1066] M. Beasley et.al.
     A 2dF spectroscopic study of globular clusters in NGC 5128: Probing the formation history of the nearest giant Elliptical
• [astro-ph:0803.1098] Z. Lorenzo
     A new luminosity function for galaxies as given by the mass-luminosity relationship
• [astro-ph:0803.1199] D. Coe et.al.
     LensPerfect: Gravitational Lens Massmap Reconstructions Yielding Exact Reproduction of All Multiple Images (could it be related to GREAT08 Challenge?)
• [astro-ph:0803.1213] H.Y.Wang et.al.
     Reconstructing the cosmic density field with the distribution of dark matter halos
• [astro-ph:0803.1420] E. Lantz et.al.
     Multi-imaging and Bayesian estimation for photon counting with EMCCD’s
• [astro-ph:0803.1491] Wu, Rozo, & Wechsler
     The Effect of Halo Assembly Bias on Self Calibration in Galaxy Cluster Surveys
• [astro-ph:0803.1616] P. Mukherjee et.al.
     Planck priors for dark energy surveys (some CHASCians would like to check!)
• [astro-ph:0803.1738] P. Mukherjee and A. R. Liddle
     Planck and reionization history: a model selection view
• [astro-ph:0803.1814] J. Cardoso et.al.
     Component separation with flexible models. Application to the separation of astrophysical emissions
• [astro-ph:0803.1851] A. R. Marble et.al.
      The Flux Auto- and Cross-Correlation of the Lyman-alpha Forest. I. Spectroscopy of QSO Pairs with Arcminute Separations and Similar Redshifts
• [astro-ph:0803.1857] R. Marble et.al.
      The Flux Auto- and Cross-Correlation of the Lyman-alpha Forest. II. Modelling Anisotropies with Cosmological Hydrodynamic Simulations

With the 5th year WMAP data release, there were many WMAP related papers and among them, most statistical papers are listed. WMAP specific/related:

• [astro-ph:0803.0586] J. Dunkley et. al.
    Five-Year Wilkinson Microwave Anisotropy Probe (WMAP) Observations: Likelihoods and Parameters from the WMAP data (likelihoods)

• [astro-ph:0803.0715] B. Gold et. al.
    Five-Year Wilkinson Microwave Anisotropy Probe (WMAP) Observations: Galactic Foreground Emission (MCMC)

• [astro-ph:0803.0889] Ichikawa, Sekiguchi, & Takahashi
    Probing the Effective Number of Neutrino Species with Cosmic Microwave Background

And others:

• [astro-ph:0802.4464] M. Sahlén et.al.
    The XMM Cluster Survey: Forecasting cosmological and cluster scaling-relation parameter constraints

• [astro-ph:0803.0918] J.M. Colberg et.al.
    The Aspen–Amsterdam Void Finder Comparison Project (TFE, tessellation field estimator)

• [astro-ph:0803.0885] J.Ballot et.al.
    On deriving p-mode parameters for inclined solar-like stars (MLE, maximum likelihood estimator)

By the way, I noticed [astro-ph:0802.4464] used Monte Carlo Markov Chain, whereas [astro-ph:0803.0715] used Markov chain Monte Carlo. Do they mean different? Or the former is a typo?