scholarly journals Statistical Inference of a Convergent Antibody Repertoire Response to Influenza Vaccine

2015 ◽  
Author(s):  
Nicolas Strauli ◽  
Ryan Hernandez
Keyword(s):  
Time Series ◽  
Influenza Vaccine ◽  
B Cell ◽  
Time Series Data ◽  
Statistical Tests ◽  
Series Data ◽  
Antibody Repertoire ◽  
Similar Response ◽  
Cell Lineages ◽  
Antigenic Stimulus

AbstractBackgroundVaccines dramatically affect an individual’s adaptive immune system, and thus provide an excellent means to study human immunity. Upon vaccination, the B cells that express antibodies (Abs) that happen to bind the vaccine are stimulated to proliferate and undergo mutagenesis at their Ab locus. This process may alter the composition of B cell lineages within an individual, which are known collectively as the antibody repertoire (AbR). Antibodies are also highly expressed in whole blood, potentially enabling unbiased RNA sequencing technologies to query this diversity. Less is known about the diversity of AbR responses across individuals to a given vaccine and if individuals tend to yield a similar response to the same antigenic stimulus.MethodsHere we implement a bioinformatic pipeline that extracts the AbR information from a time-series RNA-seq dataset of 5 patients who were administered a seasonal trivalent influenza vaccine (TIV). We harness the detailed time-series nature of this dataset and use methods based in functional data analysis (FDA) to identify the B cell lineages that respond to the vaccine. We then design and implement rigorous statistical tests in order to ask whether or not these patients exhibit a convergent AbR response to the same TIV.ResultsWe find that high-resolution time-series data can be used to help identify the Ab lineages that respond to an antigenic stimulus, and that this response can exhibit a convergent nature across patients inoculated with the same vaccine. However, correlations in AbR diversity among individuals prior to inoculation can confound inference of a convergent signal unless it is taken into account.ConclusionsWe developed a framework to identify the elements of an AbR that respond to an antigen. This information could be used to understand the diversity of different immune responses in different individuals, as well as to gauge the effectiveness of the immune response to a given stimulus within an individual. We also present a framework for testing a convergent hypothesis between AbRs; a hypothesis that is more difficult to test than previously appreciated. Our discovery of a convergent signal suggests that similar epitopes do select for antibodies with similar sequence characteristics.

scholarly journals Faktor-Faktor yang Mempengaruhi Alih Fungsi Lahan Sawah di Kabupaten Aceh Besar

2018 ◽  
Vol 3 (4) ◽  
pp. 525-533
Author(s):  
Raudhatul Husna ◽  
Azhar Azhar ◽  
Edy Marsudi
Keyword(s):  
Time Series ◽  
Population Density ◽  
Rice Field ◽  
Time Series Data ◽  
Statistical Tests ◽  
Secondary Data ◽  
Series Data ◽  
Factors Affecting ◽  
Rice Productivity ◽  
Negative Impacts

Abstrak. Alih fungsi lahan atau lazimnya disebut sebagai konversi lahan adalah  perubahan fungsi sebagian atau seluruh kawasan lahan dari fungsinya semula (seperti yang direncanakan) menjadi fungsi lain yang membawa dampak negatif terhadap lingkungan dan potensi lahan itu sendiri. Penelitian ini bertujuan untuk mengetahui apakah harga lahan, kepadatan penduduk, produktivitas padi dan jumlah PDRB dapat mempengaruhi alih fungsi lahan sawah di Kabupaten Aceh Besar. Data yang digunakan dalam penelitian ini adalah data sekunder. Data yang dikumpulkan adalah data time series dengan range tahun 2002 sampai 2016. Penelitian ini menggunakan metode analisis  regresi linier berganda. hasil penelitian dan pembahasan serta pengujian SPSS menunjukkan bahwa harga lahan, kepadatan penduduk, dan produktivitas padi berpengaruh nyata terhadap alih fungsi lahan sawah di Kabupaten Aceh Besar. sedangkan jumlah PDRB tidak berpengaruh terhadap alih fungsi lahan sawah. Hal ini ditunjukkan oleh koefisien regresi untuk variabel jumlah PDRB sebesar 0,00015. Hasil pengujian statistik menunjukkan nilai t hitung untuk jumlah PDRB sebesar 1,315 dengan nilai signifikan sebesar 0,218. Sedangkan nilai t tabel sebesar 1,782 yang berarti nilai t hitung t tabel (1,315 1,782).  Factors Affecting The Conversion Of Paddy Fields In Kabupaten Aceh Besar Abstract. Land use change or commonly referred to as land conversion is a change in the function of part or all of the land area from its original function (as planned) into other functions that bring negative impacts to the environment and the potential of the land itself. This study aims to find out whether the price of land, population density, rice productivity and the amount of GRDP can affect the conversion of rice field functions in Aceh Besar District. The data used in this research is secondary data. The data collected is time series data with range of year 2002 until 2016. This research use multiple linier regression analysis method. the results of research and discussion and testing of SPSS showed that land price, population density, and rice productivity significantly affected the conversion of wetland in Aceh Besar district. while the number of GDP does not affect the conversion of wetland. This is indicated by the regression coefficient for the GRDP variable of 0.00015. The results of statistical tests show the value of t arithmetic for the amount of GRDP by 1.315 with a significant value of 0.218. While the value of t table of 1.782 which means the value of t arithmetic t table (1,315 1.782).

ANALYSIS OF CATEGORICAL TIME-SERIES TEXT RECALL DATA USING A CONNECTIONIST MODEL

1994 ◽  
Vol 02 (03) ◽  
pp. 283-305 ◽  
Author(s):  
R.M. GOLDEN
Keyword(s):  
Time Series ◽  
Data Analysis ◽  
Statistical Model ◽  
Prior Knowledge ◽  
Time Series Data ◽  
Statistical Tests ◽  
Series Data ◽  
Connectionist Model ◽  
Recall Data ◽  
Categorical Time Series

Categorical time-series are generated by discrete-time probabilistic dynamical systems which can only be in one of a small number of finite states at any given instant in time. A novel statistical methodology based upon log-linear modelling is proposed for analyzing categorical time-series data which allows one to incorporate a considerable amount of prior knowledge directly into the data analysis. The statistical model can be shown to be formally equivalent to a connectionist (i.e., artificial neural network) model, Methods for model selection and hypothesis testing using the new statistical model for samples with large numbers of observations are then developed using asymptotic statistical theory. To illustrate this new method of categorical time-series data analysis, the model is applied to the analysis of text free recall data from children and adults. These analyses indicated that the model can successfully use the order of recalled text propositions to discriminate among alternative theories of prior knowledge and alternative treatment conditions. The reliability of the large sample statistical tests were also checked using a boot-strap methodology and found to be acceptable.

scholarly journals Entropy Analysis of High-Definition Transcranial Electric Stimulation Effects on EEG Dynamics

2019 ◽  
Vol 9 (8) ◽  
pp. 208 ◽  
Author(s):  
Diego C. Nascimento ◽  
Gabriela Depetri ◽  
Luiz H. Stefano ◽  
Osvaldo Anacleto ◽  
Joao P. Leite ◽  
...  
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Statistical Tests ◽  
Series Data ◽  
Entropy Analysis ◽  
Summary Statistic ◽  
High Definition ◽  
Stationary Characteristic ◽  
Linear Dynamical Systems ◽  
Eeg Data

A foundation of medical research is time series analysis—the behavior of variables of interest with respect to time. Time series data are often analyzed using the mean, with statistical tests applied to mean differences, and has the assumption that data are stationary. Although widely practiced, this method has limitations. Here we present an alternative statistical approach with sample analysis that provides a summary statistic accounting for the non-stationary nature of time series data. This work discusses the use of entropy as a measurement of the complexity of time series, in the context of Neuroscience, due to the non-stationary characteristic of the data. To elucidate our argument, we conducted entropy analysis on a sample of electroencephalographic (EEG) data from an interventional study using non-invasive electrical brain stimulation. We demonstrated that entropy analysis could identify intervention-related change in EEG data, supporting that entropy can be a useful “summary” statistic in non-linear dynamical systems.

Foreign Military Interventions and Suicide Attacks

2016 ◽  
Vol 61 (2) ◽  
pp. 271-297 ◽  
Author(s):  
Seung-Whan Choi ◽  
James A. Piazza
Keyword(s):  
Time Series ◽  
Local Government ◽  
Time Series Data ◽  
Statistical Tests ◽  
Balance Of Power ◽  
Series Data ◽  
Military Interventions ◽  
Government Interventions ◽  
Suicide Attacks ◽  
Cross National

This study examines the effect of foreign military interventions on the incidence of suicide attacks. It presents three theoretical explanations. Foreign military interventions may boost insurgent use of suicide attacks by (a) fomenting a nationalist backlash that sanctions the use of more extreme and unconventional tactics like suicide attacks, (b) providing more and better targets against which suicide attacks can be launched, or (c) prompting insurgents to use suicide tactics in order to overcome their power asymmetries and to confront better defended targets that are enhanced by interventions. We test these competing explanations using a battery of statistical tests on cross-national, time-series data for 138 countries during the period from 1981 to 2005. We find that only foreign interventions with specific features—pro-government interventions involving larger numbers of ground troops—boost suicide attacks in countries experiencing interventions. This finding suggests that by tipping the balance of power against insurgents and hardening targets in the context of assisting a local government, foreign military interventions are likely to increase the use of suicide attacks by regime challengers.

scholarly journals An Investigation of the Structural Characteristics of the Indian IT Sector and the Capital Goods Sector – An Application of the R Programming in Time Series Decomposition and Forecasting

2021 ◽  
Author(s):  
Jaydip Sen ◽  
Tamal Datta Chaudhuri
Keyword(s):  
Time Series ◽  
Exchange Rate ◽  
Stock Market ◽  
Time Series Data ◽  
Structural Characteristics ◽  
Statistical Tests ◽  
Strong Association ◽  
High Volume ◽  
Series Data ◽  
Capital Goods

<p>Time series analysis and forecasting of stock market prices has been a very active area of research over the last two decades. Availability of extremely fast and parallel architecture of computing and sophisticated algorithms has made it possible to extract, store, process and analyze high volume stock market time series data very efficiently. In this paper, we have used time series data of the two sectors of the Indian economy – Information Technology (IT) and Capital Goods (CG) for the period January 2009 – April 2016 and have studied the relationships of these two time series with the time series of DJIA indices, NIFTY indices and the US Dollar to Indian Rupees exchange rate. We established by graphical and statistical tests that while the IT sector of India has a strong association with DJIA indices and the Dollar to Rupee exchange rate, the Indian CG sector exhibits a strong association with the NIFTY indices. We contend that these observations corroborate our hypotheses that the Indian IT sector is strongly coupled with the world economy whereas the CG sector of India is the reflection of India’s internal economic growth. We also present several models of regression between the time series which exhibit strong association among them. The effectiveness of these models have been demonstrated by very low values of their forecasting errors. </p>

scholarly journals An Investigation of the Structural Characteristics of the Indian IT Sector and the Capital Goods Sector – An Application of the R Programming in Time Series Decomposition and Forecasting

2021 ◽  
Author(s):  
Jaydip Sen ◽  
Tamal Datta Chaudhuri
Keyword(s):  
Time Series ◽  
Exchange Rate ◽  
Stock Market ◽  
Time Series Data ◽  
Structural Characteristics ◽  
Statistical Tests ◽  
Strong Association ◽  
High Volume ◽  
Series Data ◽  
Capital Goods

<p>Time series analysis and forecasting of stock market prices has been a very active area of research over the last two decades. Availability of extremely fast and parallel architecture of computing and sophisticated algorithms has made it possible to extract, store, process and analyze high volume stock market time series data very efficiently. In this paper, we have used time series data of the two sectors of the Indian economy – Information Technology (IT) and Capital Goods (CG) for the period January 2009 – April 2016 and have studied the relationships of these two time series with the time series of DJIA indices, NIFTY indices and the US Dollar to Indian Rupees exchange rate. We established by graphical and statistical tests that while the IT sector of India has a strong association with DJIA indices and the Dollar to Rupee exchange rate, the Indian CG sector exhibits a strong association with the NIFTY indices. We contend that these observations corroborate our hypotheses that the Indian IT sector is strongly coupled with the world economy whereas the CG sector of India is the reflection of India’s internal economic growth. We also present several models of regression between the time series which exhibit strong association among them. The effectiveness of these models have been demonstrated by very low values of their forecasting errors. </p>

A New Approach for Forecasting the Price Range With Financial Interval-Valued Time Series Data

2015 ◽  
Vol 1 (2) ◽  
Author(s):  
Wei Yang ◽  
Ai Han
Keyword(s):  
Time Series ◽  
Linear Models ◽  
Time Series Data ◽  
Financial Time Series ◽  
Statistical Tests ◽  
Series Data ◽  
Daily Data ◽  
Price Range ◽  
Out Of Sample ◽  
Interval Time Series

This paper proposes an interval-based methodology to model and forecast the price range or range-based volatility process of financial asset prices. Comparing with the existing volatility models, the proposed model utilizes more information contained in the interval time series than using the range information only or modeling the high and low price processes separately. An empirical study of the U.S. stock market daily data shows that the proposed interval-based model produces more accurate range forecasts than the classic point-based linear models for range process, in terms of both in-sample and out-of-sample forecasts. The statistical tests show that the forecasting advantages of the interval-based model are statistically significant in most cases. In addition, some stability tests have been conducted for ascertaining the advantages of the interval-based model through different sample windows and forecasting periods, which reveals similar results. This study provides a new interval-based perspective for volatility modeling and forecasting of financial time series data.

scholarly journals Is the Value Added Tax System Sustainable? The Case of the Czech and Slovak Republics

2020 ◽  
Vol 12 (12) ◽  
pp. 4925 ◽  
Author(s):  
Kateřina Krzikallová ◽  
Filip Tošenovský
Keyword(s):  
Time Series ◽  
Tax Evasion ◽  
Time Series Data ◽  
Positive Impact ◽  
Statistical Tests ◽  
Value Added ◽  
Series Data ◽  
Value Added Tax ◽  
Tax System ◽  
Legislative Measures

The value added tax is an important part of revenues of the European Union and its Member States. The aim of the paper is to statistically analyse the extent of positive impact of selected legislative measures introduced in the fight against tax evasion and discuss subsequently the sustainability of the current value added tax system in the European context. The analysis was conducted for the Czech and Slovak Republics, two traditionally strong trading partners, and for an important commodity, copper. In the analysis, regression methods applied to official time series data on copper export from the Czech Republic to Slovakia were employed together with appropriate statistical tests to detect potential significance of the new legislative tools, the value added tax control statement and reverse charge mechanism. Moreover, the study considers fundamental economic factors that affect foreign trade in parallel. Based on the analysis, there is sound evidence that the major historical turnaround experienced by the time series took place due to the then forthcoming legislative measures that were to restrain the possibility of carousel frauds. The results confirm the positive impact of the measures and also suggest the necessity of more systematic changes in the tax system.

Graphical Exploratory Data Analysis for Categorical Longitudinal and Time Series Data

2013 ◽  
Author(s):  
Stephen J. Tueller ◽  
Richard A. Van Dorn ◽  
Georgiy Bobashev ◽  
Barry Eggleston
Keyword(s):  
Time Series ◽  
Data Analysis ◽  
Exploratory Data Analysis ◽  
Time Series Data ◽  
Series Data ◽  
Exploratory Data
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