scholarly journals Distance-Based Estimation Methods for Models for Discrete and Mixed-Scale Data

Entropy ◽  
2021 ◽  
Vol 23 (1) ◽  
pp. 107
Author(s):  
Elisavet M. Sofikitou ◽  
Ray Liu ◽  
Huipei Wang ◽  
Marianthi Markatou
Keyword(s):  
Data Model ◽  
Null Hypothesis ◽  
Model Misspecification ◽  
Asymptotic Properties ◽  
Estimation Methods ◽  
Measurement Scale ◽  
Interval Scale ◽  
Pearson Residuals ◽  
Using Data ◽  
Scale Data

Pearson residuals aid the task of identifying model misspecification because they compare the estimated, using data, model with the model assumed under the null hypothesis. We present different formulations of the Pearson residual system that account for the measurement scale of the data and study their properties. We further concentrate on the case of mixed-scale data, that is, data measured in both categorical and interval scale. We study the asymptotic properties and the robustness of minimum disparity estimators obtained in the case of mixed-scale data and exemplify the performance of the methods via simulation.

A multi-scale investigation of geometrically derived z0 from Hintereisferner, Austrian Alps

2020 ◽  
Author(s):  
Joshua Chambers ◽  
Mark Smith ◽  
Thomas Smith ◽  
Duncan Quincey ◽  
Jonathan Carrivick ◽  
...  
Keyword(s):  
Empirical Studies ◽  
Laser Scanner ◽  
Measurement Scale ◽  
Tuning Parameter ◽  
Glacier Surface ◽  
Multi Scale ◽  
Data Resolution ◽  
Aerodynamic Roughness ◽  
Using Data ◽  
Scale Data

<p>Spatially and temporally distributed values of glacier aerodynamic roughness (z<sub>0</sub>) are required to improve estimates of glacier melt. z<sub>0</sub>, representing the topographically-controlled height above the surface where wind speed reaches zero, is shown by empirical studies to be spatially and temporally dynamic, yet, z<sub>0</sub> is commonly overlooked as a tuning parameter in models or generalised between surfaces and over time. Indirect estimates of z<sub>0</sub> made from microtopographic measurements allow for rapid data collection over large areas but are sensitive to measurement scale, data resolution and detrending technique. The recent proliferation of remotely sensed topographic data from airborne and satellite sources has created a wealth of resources, as yet untapped in this particular field. We present a multi-scale analysis using data collected from Hintereisferner, Austria, with a view to upscaling current methods for estimating 3D microtopographic z<sub>0 </sub>so that coarser resolution, broader scale data can be used to estimate z<sub>0 </sub>at the glacier scale. Our extensive dataset covers a spectrum of scales from 5 x 5 m plots (at sub-cm resolution) to scans of almost the whole glacier surface from an in-situ terrestrial laser scanner.</p>

COMMUNITY-CURATED DATA RESOURCES AND LARGE-SCALE DATA-MODEL SYNTHESES: THE CHILDREN OF COHMAP

2016 ◽  
Author(s):  
John W. Williams ◽  
◽  
Simon Goring ◽  
Eric Grimm ◽  
Jason McLachlan
Keyword(s):  
Data Model ◽  
Large Scale ◽  
Large Scale Data ◽  
Scale Data

scholarly journals Robustness of Efficiency Scores in Data Envelopment Analysis with Interval Scale Data

2021 ◽  
Author(s):  
Nasim Nasrabadi ◽  
Akram Dehnokhalaji ◽  
Pekka Korhonen ◽  
Banu Lokman ◽  
Jyrki Wallenius
Keyword(s):  
Data Envelopment Analysis ◽  
Data Envelopment ◽  
Interval Scale ◽  
Scale Data

What drives housing prices, rent and new construction in China

2017 ◽  
Vol 10 (5) ◽  
pp. 662-686
Author(s):  
Dimitrios Staikos ◽  
Wenjun Xue
Keyword(s):  
Real Estate ◽  
Empirical Test ◽  
Housing Prices ◽  
Panel Cointegration ◽  
Real Estate Market ◽  
Estimation Methods ◽  
Content Type ◽  
New Construction ◽  
Using Data ◽  
First Time

Purpose With this paper, the authors aim to investigate the drivers behind three of the most important aspects of the Chinese real estate market, housing prices, housing rent and new construction. At the same time, the authors perform a comprehensive empirical test of the popular 4-quadrant model by Wheaton and DiPasquale. Design/methodology/approach In this paper, the authors utilize panel cointegration estimation methods and data from 35 Chinese metropolitan areas. Findings The results indicate that the 4-quadrant model is well suited to explain the determinants of housing prices. However, the same is not true regarding housing rent and new construction suggesting a more complex theoretical framework may be required for a well-rounded explanation of real estate markets. Originality/value It is the first time that panel data are used to estimate rent and new construction for China. Also, it is the first time a comprehensive test of the Wheaton and DiPasquale 4-quadrant model is performed using data from China.

scholarly journals Research-ready data for multi-cohort analyses: The Dementias Platform UK (DPUK) C-Surv data model

2021 ◽  
Author(s):  
Sarah Bauermeister ◽  
Joshua R Bauermeister ◽  
R Bridgman ◽  
C Felici ◽  
M Newbury ◽  
...  
Keyword(s):  
Data Model ◽  
Data Access ◽  
Data Discovery ◽  
Standard Data ◽  
Technology Standard ◽  
Access Request ◽  
Nested Structure ◽  
Using Data ◽  
Model C ◽  
Cohort Analyses

Abstract Research-ready data (that curated to a defined standard) increases scientific opportunity and rigour by integrating the data environment. The development of research platforms has highlighted the value of research-ready data, particularly for multi-cohort analyses. Following user consultation, a standard data model (C-Surv), optimised for data discovery, was developed using data from 12 Dementias Platform UK (DPUK) population and clinical cohort studies. The model uses a four-tier nested structure based on 18 data themes selected according to user behaviour or technology. Standard variable naming conventions are applied to uniquely identify variables within the context of longitudinal studies. The data model was used to develop a harmonised dataset for 11 cohorts. This dataset populated the Cohort Explorer data discovery tool for assessing the feasibility of an analysis prior to making a data access request. It was concluded that developing and applying a standard data model (C-Surv) for research cohort data is feasible and useful.

scholarly journals The Influence of Social Media Against Interest in Buying Kopilogi Products

2021 ◽  
Vol 3 (1) ◽  
pp. 75-83
Author(s):  
Salma Fitri Nurfauziah ◽  
Nizar Alam Hamdani
Keyword(s):  
Social Media ◽  
Secondary Data ◽  
Measurement Scale ◽  
Primary Data ◽  
Interval Scale ◽  
Online Questionnaire ◽  
Analysis Technique ◽  
Data Collection Technique ◽  
Strongly Agree ◽  
The Relationship

This study discusses the influence of social media on interest in buying copilogy products. The relationship used in this study is a causal relationship with 60 Garut domiciled consumer respondents who have already tried their products. The data analysis technique used is simple regression with the application of SPSS 20. The measurement scale used by researchers is the interval scale. This study uses primary data and secondary data obtained from books, journals, literature, scientific works from the internet with relevant sources. The primary data collection technique in this study is an online questionnaire that contains a number of structured statements given to respondents through Google forms and respondents provide answers based on a Likert scale of 1-5, starting from 1 (strongly disagree) to 5 (strongly agree). The conclusion of this research is the significant effect between the influence of Social Media on Kopilogi Buy Interest.

scholarly journals On accuracy of upper quantiles estimation

2010 ◽  
Vol 7 (4) ◽  
pp. 4761-4784
Author(s):  
I. Markiewicz ◽  
W. G. Strupczewski ◽  
K. Kochanek
Keyword(s):  
Distribution Function ◽  
Population Distribution ◽  
Model Misspecification ◽  
Estimation Method ◽  
Flood Frequency ◽  
Likelihood Method ◽  
Flood Frequency Analysis ◽  
Estimation Methods ◽  
Hypothetical Model ◽  
Log Normal

Abstract. Flood frequency analysis (FFA) entails estimation of the upper tail of a probability density function (PDF) of annual peak flows obtained from either the annual maximum series or partial duration series. In hydrological practice the properties of various estimation methods of upper quantiles are identified with the case of known population distribution function. In reality the assumed hypothetical model differs from the true one and one can not assess the magnitude of error caused by model misspecification in respect to any estimated statistics. The opinion about the accuracy of the methods of upper quantiles estimation formed from the case of known population distribution function is upheld. The above-mentioned issue is the subject of the paper. The accuracy of large quantile assessments obtained from the four estimation methods are compared for two-parameter log-normal and log-Gumbel distributions and their three-parameter counterparts, i.e., three-parameter log-normal and GEV distributions. The cases of true and false hypothetical model are considered. The accuracy of flood quantile estimates depend on the sample size, on the distribution type, both true and hypothetical, and strongly depend on the estimation method. In particular, the maximum likelihood method looses its advantageous properties in case of model misspecification.

scholarly journals Two 2-D DOA Estimation Methods with Full and Partial Generalized Virtual Aperture Extension Technology

2019 ◽  
Vol 2019 ◽  
pp. 1-11
Author(s):  
Riheng Wu ◽  
Yangyang Dong ◽  
Zhenhai Zhang ◽  
Le Xu
Keyword(s):  
Data Model ◽  
Doa Estimation ◽  
Nonsingular Matrix ◽  
Eigenvalue Decomposition ◽  
Estimation Methods ◽  
Sparse Reconstruction ◽  
Invariance Property ◽  
Reconstruction Method ◽  
Uniqueness Of Solutions ◽  
Moderate Complexity

We address the two-dimensional direction-of-arrival (2-D DOA) estimation problem for L-shaped uniform linear array (ULA) using two kinds of approaches represented by the subspace-like method and the sparse reconstruction method. Particular interest emphasizes on exploiting the generalized conjugate symmetry property of L-shaped ULA to maximize the virtual array aperture for two kinds of approaches. The subspace-like method develops the rotational invariance property of the full virtual received data model by introducing two azimuths and two elevation selection matrices. As a consequence, the problem to estimate azimuths represented by an eigenvalue matrix can be first solved by applying the eigenvalue decomposition (EVD) to a known nonsingular matrix, and the angles pairing is automatically implemented via the associate eigenvector. For the sparse reconstruction method, first, we give a lemma to verify that the received data model is equivalent to its dictionary-based sparse representation under certain mild conditions, and the uniqueness of solutions is guaranteed by assuming azimuth and elevation indices to lie on different rows and columns of sparse signal cross-correlation matrix; we then derive two kinds of data models to reconstruct sparse 2-D DOA via M-FOCUSS with and without compressive sensing (CS) involvements; finally, the numerical simulations validate the proposed approaches outperform the existing methods at a low or moderate complexity cost.

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