Parametric statistics

About: Parametric statistics is a research topic. Over the lifetime, 39200 publications have been published within this topic receiving 765761 citations.

Location of mobile terminals using time measurements and survey points

Abstract: Mobile terminal location has attracted much interest for its applications in emergency communications, location-sensitive browsing, and resource allocation. The paper introduces the use of nonparametric kernel-based estimators for location of mobile terminals using measurements of propagation delays. It is demonstrated that these estimators perform better than the previously used parametric maximum likelihood estimators for the case of a simulated microcell environment with line-of-sight (LOS) and non-line-of-sight (NLOS) radio propagation at several different levels of measurement noise. Their performance is not greatly degraded by NLOS effects. Methods for calculating good values for parameters of the kernel functions are demonstrated, as well as the robustness of the estimators when the values of the parameters vary from the optimal points. A lower bound on the mean square error of location estimation that considers the transition between LOS to NLOS propagation over short distances is presented. It is demonstrated the proposed location estimation method comes close to meeting this bound.

Nonparametric Estimation and Symmetry Tests for Conditional Density Functions

Abstract: We suggest two new methods for conditional density estimation. The first is based on locally fitting a log-linear model, and is in the spirit of recent work on locally parametric techniques in density estimation. The second method is a constrained local polynomial estimator. Both methods always produce non-negative estimators. We propose an algorithm suitable for selecting the two bandwidths for either estimator. We also develop a new bootstrap test for the symmetry of conditional density functions. The proposed methods are illustrated by both simulation and application to a real data set.

Comparing Inference Approaches for RD Designs: A Reexamination of the Effect of Head Start on Child Mortality

Abstract: The regression discontinuity (RD) design is a popular quasi-experimental design for causal inference and policy evaluation. The most common inference approaches in RD designs employ “flexible” parametric and nonparametric local polynomial methods, which rely on extrapolation and large-sample approximations of conditional expectations using observations somewhat near the cutoff that determines treatment assignment. An alternative inference approach employs the idea of local randomization, where the very few units closest to the cutoff are regarded as randomly assigned to treatment and finite-sample exact inference methods are used. In this paper, we contrast these approaches empirically by re-analyzing the influential findings of Ludwig and Miller (2007), who studied the effect of Head Start assistance on child mortality employing parametric RD methods. We first review methods based on approximations of conditional expectations, which are relatively well developed in the literature, and then present new methods based on randomization inference. In particular, we extend the local randomization framework to allow for parametric adjustments of the potential outcomes; our extended framework substantially relaxes strong assumptions in prior literature and better resembles other RD inference methods. We compare all these methods formally, focusing on both estimands and inference properties. In addition, we develop new approaches for randomization-based sensitivity analysis specifically tailored to RD designs. Applying all these methods to the Head Start data, we find that the original RD treatment effect reported in the literature is quite stable and robust, an empirical finding that enhances the credibility of the original result. All the empirical methods we discuss are readily available in general purpose software in R and Stata; we also provide the dataset and software code needed to replicate all our results.

Effect of operating conditions on the characteristics of pressure fluctuations in a bubble column

Abstract: The effects of operating parameters on the basic statistical characteristics of pressure fluctuations were studied for different measurement configurations in a bubble column of diameter 0.292 m. Different sources of the pressure signal were identified using cross-spectral analysis. A simple parametric method was proposed for on-line flow pattern identification based on the optimal order of the autoregressive model.

Velocity and acceleration of height growth using kernel estimation

Abstract: SummaryA method is introduced for estimating acceleration, velocity and distance of longitudinal growth curves and it is illustrated by analysing human height growth. This approach, called kernel estimation, belongs to the class of smoothing methods and does not assume an a priori fixed functional model, and not even that one and the same model is applicable for all children. The examples presented show that acceleration curves might allow a better quantification of the mid-growth spurt (MS) and a more differentiated analysis of the pubertal spurt (PS). Accelerations are prone to follow random variations present in the data, and parameters defined in terms of acceleration are, therefore, validated by a comparison with parameters defined in terms of velocity. Our non-parametric-curve-fitting approach is also compared with parametric fitting via a model suggested by Preece and Baines (1978).