#StatModeling

[statmodeller]

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[classychaosnachos]

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[frankpodmore]

♲ Tarik Abou-Chadi (tabouchadi@twitter.com)2021-05-08 10:44:16:

@jrhopkin @simonjhix @gabyhinsliff @StatModeling @chrishanretty @benwansell @drjennings @StephenDFisher This is what party attachment and vote look like based on Oesch class groups in the last round of the ESS. No idea how one would spin this into "Labour has lost all of its working class appeal" you will probably get something a little longer on the comparative perspective soon😉

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[malvikasaraf7-blog]

Sample

The sample is from the National Stock Exchange’s leading Index, NIFTY 50 since this index is used by most of the investors in India. 41 companies have been studied out of the 50 companies since these companies have consistently been a part of NIFTY 50 over the years. Observations (N=1978) represented the OHLC (opening, high, low and closing) of stock prices for these 41 companies, which included 1025 days of Bullish period and 953 days of Bearish period. The data analytic sample for this study included stock prices for 1978 days from 1st April 2008 to 31st March 2016 for the 41 companies under Bearish and Bullish periods.

Procedure

Data were collected from (https://www1.nseindia.com/products/content/equities/equities/eq_security.htm), the website of National Stock Exchange of India Ltd. on daily stock prices of 41 companies out of NIFTY 50 companies from 1st April 2008 to 31st March 2016.

Measures

Here the response variable is Herd Mentality in Indian stock market data using explanatory variable Cross-Sectional Absolute Deviation (CSAD). Herd Mentality was assessed on the basis of daily stock market data of NIFTY 50 companies during Bearish and Bullish period for the given time period. Least Squares technique was applied to find the corresponding p values for Bearish, Bullish and the whole period using statmodels in Python. Also, by using matplotlib in Python, a non-increasing non-linear relationship between CSAD and absolute values on market return was observed in a scatter plot.

[mft-toyama]

via @hankagosa

ブログ更新しました → COVID-19 日本人の潜在的な陽性者数を推定する試み - StatModeling Memorandum https://t.co/tO1YUBreSX

— Kentaro Matsuura (@hankagosa) April 4, 2020

[mrhenryharrell]

RT @NAChristakis: For example, for a very sophisticated estimate of the CFR, based on all data and using a good method, see this discussion (and link to paper) by @StatModeling (CFR is ~1.5% in this estimate!): https://t.co/z3D25i2UqM 7/ https://t.co/MufUezFgFc

RT @NAChristakis: For example, for a very sophisticated estimate of the CFR, based on all data and using a good method, see this discussion (and link to paper) by @StatModeling (CFR is ~1.5% in this estimate!): https://statmodeling.stat.columbia.edu/2020/03/07/coronavirus-age-specific-fatality-ratio-estimated-using-stan/ 7/ from @gueringreen - twitter https://twitter.com/GuerinGreen/status/1240047639615262720

[hellodistinguishedstudentllama]

RT @NAChristakis: For example, for a very sophisticated estimate of the CFR, based on all data and using a good method, see this discussion (and link to paper) by @StatModeling (CFR is ~1.5% in this estimate!): https://t.co/z3D25i2UqM 7/ https://t.co/MufUezFgFc

RT @NAChristakis: For example, for a very sophisticated estimate of the CFR, based on all data and using a good method, see this discussion (and link to paper) by @StatModeling (CFR is ~1.5% in this estimate!): https://statmodeling.stat.columbia.edu/2020/03/07/coronavirus-age-specific-fatality-ratio-estimated-using-stan/ 7/ from FB Mashes https://twitter.com/GuerinGreen/status/1240047639615262720 https://ift.tt/3b2Dkbm

[craigbrownphd-blog-blog]

Hands-on python: my preamble

#ICYDK: I prefer to structure my code the same way as an article, and if have academic background as well, you can relate. Hence, I usually start with preamble, where I put all the packages and toolkits I would like to use. Then, the main part follows and subsequently the rest (for example, where the result of the model should go, all the connections). Especially if you are getting started and learn python, I recommend to structure and comment your code as clear as possible. With Jupiter you have several options for headings and comments already implemented in the notebook. Clear structure and sufficient comments would help you to write the code which you can easily recap (and follow) in months or even years. My experience is that good codes are often (partly) recycled. In this blog post, I summarized what I call, (1) general preamble and (2) visualization preamble. Although I post (3) my map and (4) analysis preamble as well, I will be more detailed about them in the forthcoming posts. 1. General preamble import pandas as pd import numpy as np import datetime as dt I need pandas and numpy always. Therefore, I always start with these two. I work often with time series. That’s why I need the third line. With datetime you can define the date, set an index on date and do all other date relevant manipulations.  2. Visualization import seaborn as sns import matplotlib.pyplot as plt %matplotlib inline These lines are the very general, with seaborn and matplotlib we will cover most of visualizations, of any kind. Then, we can become more specific and define the style and context. Refer to seaborn page for details. I prefer ‘whitegrid’ and ‘talk’ and set them in the beginning as my personized default framework. Refer to the seaborn for further options. sns.set_style('whitegrid') sns.set_context('talk')  Furthermore, if you work for the company which have corporate identity with predefined colors, it is useful to create your own palette with company colors. The graphs would automatically get these colors (as long as you not overrule it). The corporate identity colors at my company are ordered in an agreed way. So, by the ordering in the list you define the order in which the colors are applied automatically. mycolors = ['#0076a7','#ae5b3a','#CD997C','#EDD195'] #list of colors, I prefer hex numbers for colors sns.set_palette(mycolors) # define the palette sns.palplot(sns.color_palette()) # display 3. Maps Maps are special kind of visualization, they worth a separate blog post (forthcoming). You will find my preamble for them below. import folium from folium.plugins import MarkerCluster  #if you want to cluster from folium.plugins import MiniMap  # cool minimap in the low right corner of the big map from folium import plugins from folium import FeatureGroup  #if you customize 4. Analysis I am time series econometrician. Therefore, my analysis preamble is very time series biased. You start time series analysis with tests to understand the autocorrelation structure of your variables. I would be more detailed on data analysis with python in my forthcoming blogs. import statsmodels.api as sm from statsmodels.tsa.stattools import adfuller  #Augmented Dickey-Fuller unit root test from statsmodels.graphics.tsaplots import plot_acf #autocorrelation function from statsmodels.graphics.tsaplots import plot_pacf  #partial autocorrelation from statsmodels.tsa.api import VAR, DynamicVAR  #for time series analysis You will find a lot useful explanations and relavant packages on statmodels webpage. http://bit.ly/36CUlre

[copyoffice]

Age Heaping（エイジ・ヒーピング）とは、図1（※インドネシアの人口ピラミッド）に見られるように、人口ピラミッドなどの年齢各歳別の統計において、例えば50 歳、55歳など、0または5で終わる年齢において、人口が突出して多くなる現象のことである。

この現象が起こる原因は、その国において、自分の年齢を正確に知らない人が多いことである。このため、自分の年齢を回答する場合には、50歳、55歳などと、自分の年齢に近いと思われる、切りの良い数字で回答することになるわけである。

この現象は、発展途上国に多く見られる。ただし、発展途上国であっても、十二支が社会的習慣として浸透している国では、この現象は見られない。

(via 人口ピラミッドのAge Heapingを階層ベイズで補正する - StatModeling Memorandum)

[statmodeller]

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[kjunichi]

TensorFlowで統計モデリング - StatModeling Memorandum [はてなブックマーク]

TensorFlowで統計モデリング - StatModeling Memorandum

とある勉強会で「TensorFlowで統計モデリング」というタイトルで講義をしました。聴衆はPythonユーザが多く、データ量が大きい問題が多そうだったので、StanよりもTensorFlowで点推定するスキルを伸ばすとメリットが大きいだろうと思ってこのようなタイトルになりました。 発表資料は以下になります。 TensorFlowで統���...

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[infogrexnew]

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[tutorindia2020]

How to import CSV File in python and does stat model - in Tamil

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[murderofcro-ws]

Favorite tweets: So about that miraculous prediction from Michael Levitt.Via @StatModeling https://t.co/SBsuWpb8fT pic.twitter.com/w22pOZc6BK— Carl T. Bergstrom (@CT_Bergstrom) April 1, 2020

http://twitter.com/CT_Bergstrom

[interglacial]

via Twitter https://twitter.com/richard_raw