scholarly journals Asymptotic normality of the relative error regression function estimator for censored and time series data

2021 ◽  
Vol 9 (1) ◽  
pp. 156-178
Author(s):  
Feriel Bouhadjera ◽  
Elias Ould Saïd
Keyword(s):  
Asymptotic Normality ◽  
Relative Error ◽  
Time Series Data ◽  
Asymptotic Variance ◽  
Kernel Estimator ◽  
Regression Function ◽  
Real Data ◽  
Series Data ◽  
Failure Times

Abstract Consider a survival time study, where a sequence of possibly censored failure times is observed with d-dimensional covariate The main goal of this article is to establish the asymptotic normality of the kernel estimator of the relative error regression function when the data exhibit some kind of dependency. The asymptotic variance is explicitly given. Some simulations are drawn to lend further support to our theoretical result and illustrate the good accuracy of the studied method. Furthermore, a real data example is treated to show the good quality of the prediction and that the true data are well inside in the confidence intervals.

scholarly journals EL NINO, LA NINA, DAN PENAWARAN PANGAN DI JAWA, INDONESIA

2011 ◽  
Vol 12 (2) ◽  
pp. 257
Author(s):  
Arini Wahyu Utami ◽  
Jamhari Jamhari ◽  
Suhatmini Hardyastuti
Keyword(s):  
El Niño ◽  
Time Series Data ◽  
El Nino ◽  
Southern Oscillation ◽  
Regression Function ◽  
La Niña ◽  
Series Data ◽  
Cross Sectional ◽  
La Nina ◽  
The Impact

Paddy and maize are two important food crops in Indonesia and mainly produced in Java Island. This research aimed to know the impact of El Nino and La Nina on paddy and maize farmer’s supply in Java. Cross sectional data from four provinces in Java was combined with time series data during 1987-2006. Paddy supply was estimated using log model, while maize supply used autoregressive model; each was estimated using two types of regression function. First, it included dummy variable of El Nino and La Nina to know their influence into paddy and maize supply. Second, Southern Oscillation Index was used to analyze the supply changing when El Nino or La Nina occur. The result showed that El Nino and La Nina did not influence paddy supply, while La Nina influenced maize supply in Java. Maize supply increased when La Nina occurred.

scholarly journals How to Identify Varying Lead–Lag Effects in Time Series Data: Implementation, Validation, and Application of the Generalized Causality Algorithm

Algorithms ◽  
2020 ◽  
Vol 13 (4) ◽  
pp. 95 ◽  
Author(s):  
Johannes Stübinger ◽  
Katharina Adler
Keyword(s):  
Time Series ◽  
Large Scale ◽  
Structural Breaks ◽  
Time Series Data ◽  
Consumer Price Index ◽  
Real Data ◽  
Linear Mapping ◽  
Series Data ◽  
Lag Effects ◽  
Silver Metal

This paper develops the generalized causality algorithm and applies it to a multitude of data from the fields of economics and finance. Specifically, our parameter-free algorithm efficiently determines the optimal non-linear mapping and identifies varying lead–lag effects between two given time series. This procedure allows an elastic adjustment of the time axis to find similar but phase-shifted sequences—structural breaks in their relationship are also captured. A large-scale simulation study validates the outperformance in the vast majority of parameter constellations in terms of efficiency, robustness, and feasibility. Finally, the presented methodology is applied to real data from the areas of macroeconomics, finance, and metal. Highest similarity show the pairs of gross domestic product and consumer price index (macroeconomics), S&P 500 index and Deutscher Aktienindex (finance), as well as gold and silver (metal). In addition, the algorithm takes full use of its flexibility and identifies both various structural breaks and regime patterns over time, which are (partly) well documented in the literature.

scholarly journals Growth Limits: A Conceptual Analysis for Sustainable Development in Nigeria

SAGE Open ◽  
2020 ◽  
Vol 10 (2) ◽  
pp. 215824402091827
Author(s):  
Oluwabunmi O. Adejumo
Keyword(s):  
Time Series Data ◽  
Optimal Growth ◽  
Series Data ◽  
Data Sets ◽  
African Countries ◽  
Limits To Growth ◽  
Development Indicators ◽  
Long Run ◽  
Sub Saharan

In the school of development thought, growth has been identified as a viable alternative to the challenge of poverty and economic backwardness. However, the ecologists have continuously challenged the growth position in relation to environmental degradation and depletion. It is against this background; this study examined the limits to growth in Nigeria beyond which there will be inimical consequences for the environment. The study employed time series data that spanned between 1970 and 2014. These data sets were sourced from the World Development Indicators. Based on the assimilation model, threshold estimates were used to identify optimal growth regions, whereas regression estimates were used to measure growth effects. It was discovered that below the identified growth limit, there are currently significant negative impacts on the quality of the environment in Nigeria via economic growth. This study is a single-country case, that is, Nigeria; hence, the study can be expanded to include other sub-Saharan African countries. The study adds to knowledge by establishing the prospects for sustainability in the quality of the environment in the long run; therefore, policies designed in this areas have higher likelihood of attaining sustainability.

scholarly journals Accounting Implications of Micro-Fiscal Measures and Quality of Real Gross National Goods and Services: Empirical Evidence From Nigeria

2020 ◽  
Vol 11 (6) ◽  
pp. 155
Author(s):  
Akabom I. Asuquo ◽  
Arzizeh Tiesieh Tapang ◽  
Uwem E. Uwah ◽  
Nicholas O. Dan ◽  
Ashishie Peter Uklala
Keyword(s):  
Empirical Evidence ◽  
Inverse Relationship ◽  
Time Series Data ◽  
Series Data ◽  
Government Agencies ◽  
Exploratory Research ◽  
Direct Effects ◽  
Goods And Services ◽  
The Government

The study explored into accounting implications of micro-fiscal measures and quality of real gross national goods and services: empirical evidence from Nigeria for a period of thirty years. The objective was to examine how micro-fiscal measures affect real gross national goods and services using thirty years’ time-series data. The exploratory research methodology was applied and data collected were analysed using multiple regression and other statistical techniques. Findings of the study revealed that significant and direct effects were exerted on gross national goods and services by all the known and identified micro-fiscal measures in the review, except swap and levy ratios which had inverse relationship as revealed by their coefficients obtained from the analysis. Therefore, the government and government agencies have a duty to control macro-fiscal activities in terms of creation of national goods, wealth and services using the identified micro-fiscal mechanisms as the basis for decisions and policies making besides implementation.

scholarly journals Forecast Quality of Physics-Based and Data-Driven PV Performance Models for a Small-Scale PV System

2021 ◽  
Vol 9 ◽  
Author(s):  
Moritz Stüber ◽  
Felix Scherhag ◽  
Matthieu Deru ◽  
Alassane Ndiaye ◽  
Muhammad Moiz Sakha ◽  
...  
Keyword(s):  
Smart Grids ◽  
Time Series Data ◽  
Short Term Memory ◽  
Data Driven ◽  
Series Data ◽  
Small Scale ◽  
Pv System ◽  
Advantages And Disadvantages ◽  
Forecast Quality

In the context of smart grids, the need for forecasts of the power output of small-scale photovoltaic (PV) arrays increases as control processes such as the management of flexibilities in the distribution grid gain importance. However, there is often only very little knowledge about the PV systems installed: even fundamental system parameters such as panel orientation, the number of panels and their type, or time series data of past PV system performance are usually unknown to the grid operator. In the past, only forecasting models that attempted to account for cause-and-effect chains existed; nowadays, also data-driven methods that attempt to recognize patterns in past behavior are available. Choosing between physics-based or data-driven forecast methods requires knowledge about the typical forecast quality as well as the requirements that each approach entails. In this contribution, the achieved forecast quality for a typical scenario (day-ahead, based on numerical weather predictions [NWP]) is evaluated for one physics-based as well as five different data-driven forecast methods for a year at the same site in south-western Germany. Namely, feed-forward neural networks (FFNN), long short-term memory (LSTM) networks, random forest, bagging and boosting are investigated. Additionally, the forecast quality of the weather forecast is analyzed for key quantities. All evaluated PV forecast methods showed comparable performance; based on concise descriptions of the forecast approaches, advantages and disadvantages of each are discussed. The approaches are viable even though the forecasts regularly differ significantly from the observed behavior; the residual analysis performed offers a qualitative insight into the achievable forecast quality in a typical real-world scenario.

scholarly journals Inferring phage-bacteria infection networks from time series data

2016 ◽  
Author(s):  
Luis F. Jover ◽  
Justin Romberg ◽  
Joshua S. Weitz
Keyword(s):  
Time Series ◽  
Time Course ◽  
Community Dynamics ◽  
Time Series Data ◽  
Plaque Assay ◽  
Interaction Network ◽  
Series Data ◽  
Factors Affecting ◽  
Evolutionary Changes

In communities with bacterial viruses (phage) and bacteria, the phage-bacteria infection network establishes which virus types infects which host types. The structure of the infection network is a key element in understanding community dynamics. Yet, this infection network is often difficult to ascertain. Introduced over 60 years ago, the plaque assay remains the gold-standard for establishing who infects whom in a community. This culture-based approach does not scale to environmental samples with increased levels of phage and bacterial diversity, much of which is currently unculturable. Here, we propose an alternative method of inferring phage-bacteria infection networks. This method uses time series data of fluctuating population densities to estimate the complete interaction network without having to test each phage-bacteria pair individually. We use in silico experiments to analyze the factors affecting the quality of network reconstruction and find robust regimes where accurate reconstructions are possible. In addition, we present a multi-experiment approach where time series from different experiments are combined to improve estimates of the infection network and mitigate against the possibility of evolutionary changes to infection during the time-course of measurement.

scholarly journals Persepsi Kualitas Mahasiswa Program Studi DIII Akuntansi Polines Selama Praktek Kerja Lapangan (PKL) di Industri

2018 ◽  
Vol 1 (1) ◽  
Author(s):  
Moh.Hasanudin Marliyati ◽  
Sri Murtini ◽  
Resi Yudhaningsih ◽  
Retno Retno
Keyword(s):  
Information Technology ◽  
Time Series ◽  
Communication Skills ◽  
Time Series Data ◽  
Soft Skills ◽  
Series Data ◽  
Central Java ◽  
Hard Skills ◽  
Main Components

<p>This research aimed at exploring the quality of accounting diploma <br />students during their internship program in industries. The term of student’s <br />quality described in this research isexplained using 5 main components as follows: (1) communication skills (2) teamwork (3) independence (4) creativity (5) accounting and information technology (IT)-related skills. The research’s sample is industries where students of Diploma in Accounting of State Polytechnic of Semarang (SPS) took their intership and the students themselves whom have completed their internship program for three months in various institutions such as private enterprises, state owned enterprises, local government offices spread out around Central Java. The data on this research is time series data taken from 2015 to 2016 and was collected using questionnaires from the corresponding industries about the students competencies both hard skills and soft skills. <br />Data was scored using Likert scale, ranges from Poor (1) to Excellent (5) and <br />analyzed using statistic descriptive. The result showed that average students’ <br />quality during their internship was good. Among the 5 skills observed, the <br />corresponding industries ranked teamwork skills as the highest, followed by <br />independence, creativity, communication skills and the accounting and IT -related skills. It is expected that the result can be used for future development of Accounting Program Study of SPS.</p>

scholarly journals Time series forecasting of styrene price using a hybrid ARIMA and neural network model

2019 ◽  
Vol 10 (3) ◽  
pp. 915
Author(s):  
Ali Ebrahimi Ghahnavieh
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Real Data ◽  
Time Series Forecasting ◽  
Series Data ◽  
Data Sets ◽  
Combined Model ◽  
Future Behavior ◽  
Hybrid Forecasting ◽  
Ann Models

Every player in the market has a greater need to know about the smallest change in the market. Therefore, the ability to see what is ahead is a valuable advantage. The purpose of this research is to make an attempt to understand the behavioral patterns and try to find a new hybrid forecasting approach based on ARIMA-ANN for estimating styrene price. The time series analysis and forecasting is an essential tool which could be widely useful for finding the significant characteristics for making future decisions. In this study ARIMA, ANN and Hybrid ARIMA-ANN models were applied to evaluate the previous behavior of a time series data, in order to make interpretations about its future behavior for styrene price. Experimental results with real data sets show that the combined model can be most suitable to improve forecasting accurateness rather than traditional time series forecasting methodologies. As a subset of the literature, the small number of studies have been done to realize the new forecasting methods for forecasting styrene price.

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