The Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), and the Deviance Information Criterion (DIC) are perhaps the most widely-used information criteria (IC) in model building and selection. A fourth, Minimum Description Length (MDL), is closely related to the BIC. In a nutshell, they provide guidance as which alternative model provides the most "bang for buck," i.e., the best fit after penalizing for model complexity. Penalizing for complexity is important since, given candidate models of similar predictive or explanatory power, the simplest model is most likely to be the best choice. In line with Occam's razor, complex models sometimes perform poorly on data not used in the model building. There are several others, including AIC3, SABIC, and CAIC, and no clear consensus among authorities as far as I am aware as to which is "best" overall. IC will not necessarily agree on which model should be chosen. Cross-validation, Predicted Residual Error Sum of Squares (PRESS) statistic, a kind of cross-validation, and Mallows’ Cp are also used instead of IC. Information criteria are covered in varying levels in detail in most statistics textbooks and are the subject of numerous academic papers. I know of no single go-to source on this topic.
The Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), and the Deviance Information Criterion (DIC) are perhaps the most widely-used information criteria (IC) in model building and selection. A fourth, Minimum Description Length (MDL), is closely related to the BIC. In a nutshell, they provide guidance as which alternative model provides the most "bang for buck," i.e., the best fit after penalizing for model complexity. Penalizing for complexity is important since, given candidate models of similar predictive or explanatory power, the simplest model is most likely to be the best choice. In line with Occam's razor, complex models sometimes perform poorly on data not used in the model building. There are several others, including AIC3, SABIC, and CAIC, and no clear consensus among authorities as far as I am aware as to which is "best" overall. IC will not necessarily agree on which model should be chosen. Cross-validation, Predicted Residual Error Sum of Squares (PRESS) statistic, a kind of cross-validation, and Mallows’ Cp are also used instead of IC. Information criteria are covered in varying levels in detail in most statistics textbooks and are the subject of numerous academic papers. I know of no single go-to source on this topic.
n the U.S. people generally use Bitcoin as an alternative investment, helping diversify a portfolio apart from stocks and bonds. You can also use Bitcoin to make purchases, but the number of vendors that accept the cryptocurrency is still limited. Big companies that accept Bitcoin include Overstock, AT&T and Twitch. You may also find that some small local retailers or certain websites take Bitcoin, but you’ll have to do some digging. That said, PayPal has announced that it will enable cryptocurrency as a funding source for purchases this year, financing purchases by automatically converting crypto holdings to fiat currency for users. “They have 346 million users and they’re connected to 26 million merchants,” says Spencer Montgomery, founder of Uinta Crypto Consulting. “It’s huge.”
The SSE was the first modern stock exchange to open in China, with trading commencing in 1990. It has now grown to become the largest stock exchange in Asia and the third-largest in the world by market capitalization, which stood at RMB 50.6 trillion (US$7.8 trillion) as of September 2021. Stocks (both A-shares and B-shares), bonds, funds, and derivatives are traded on the exchange. The SEE has two trading boards, the Main Board and the Science and Technology Innovation Board, the latter more commonly known as the STAR Market. The Main Board mainly hosts large, well-established Chinese companies and lists both A-shares and B-shares.
